fix: MoE GPU-mode TPS uses full model size + expert-count-scaled overhead (#464)
* fix: MoE offload speed estimate accounts for DDR bandwidth bottleneck Previously, MoE offload speed was estimated using GPU VRAM bandwidth divided by active parameters with a flat 0.8 factor. This produced unrealistically high estimates (e.g. 80 tok/s for Qwen3-Next-80B on 16GB VRAM). In practice, active experts change per token and must be fetched from system RAM across the PCIe bus. The bottleneck is DDR bandwidth, not GPU bandwidth. Measured: 15.4 tok/s actual vs 80 tok/s estimated. New model: per-token time = expert_fetch_time + gpu_compute_time - expert_fetch = active_expert_gb / ddr_bandwidth (default 50 GB/s) - gpu_compute = active_expert_gb / gpu_bandwidth Result: 15.2 tok/s estimated vs 15.4 tok/s measured on RX 6900 XT. GPU mode for MoE models also corrected: uses full model size for bandwidth calculation since runtimes don't do expert-aware placement. DDR bandwidth is configurable via LLMFIT_DDR_BANDWIDTH env var. All 300 existing tests pass. Co-authored-by: AlexsJones <1235925@users.noreply.github.com> (Addresses review nits: extracted ddr_bandwidth_gbps() helper, removed unreachable!() in favor of config.run_mode_factors) 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta Code <noreply@letta.com> * chore: enforce LF line endings via .gitattributes * fix: MoE GPU-mode TPS uses disk size + expert cache penalty Two changes to the MoE GPU-mode speed estimate: 1. Use model.estimate_disk_gb() instead of params_b * bpp — MoE routing tables and shared embeddings make the actual quantized file larger than total_params * bytes_per_param. 2. Apply 0.65 MoE overhead factor for cache thrashing from 128+ experts causing irregular memory access patterns. Result: Qwen3-30B-A3B Q2_K on RX 6900 XT now estimates 16.2 tok/s (measured: 16.3 tok/s) — was 337 tok/s before. All 300 tests pass. 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta Code <noreply@letta.com> * fix: scale MoE overhead by expert count + apply RunModeFactors to GPU path The flat 0.65 moe_overhead penalized all MoE models equally — Mixtral 8x7B (8 experts) got the same 35% penalty as Qwen3-30B-A3B (128 experts). Now scales by num_experts: 0.95 for ≤8, stepping down to 0.65 for 128+. Apply RunModeFactors only to the GPU MoE path (default factor 1.0 = no behavior change). The MoeOffload paths already model DDR bottleneck explicitly via physics-based formula, so applying mode_factor there would double-count the offload penalty (0.8 default would silently cut estimates by 20% and invalidate the calibration data). Also fixes misleading comment: estimate_disk_gb uses total params, not "GGUF block quantization overhead" as previously claimed. All 300 tests pass. Addresses review findings from adversarial code review of issue #449. 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta Code <noreply@letta.com> * docs: add DDR bandwidth env var override example to doc comment Per review feedback from three-foxes-in-a-trenchcoat. 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta Code <noreply@letta.com> * refactor: rename model_gb to active_gb + add efficiency coupling comment Per AlexsJones review feedback: - Rename model_gb -> active_gb to clarify it's active params, not full model - Replace hardcoded 0.55 with named fallback_efficiency + COUPLING comment 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta Code <noreply@letta.com> * style: fix rustfmt formatting 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta Code <noreply@letta.com> --------- Co-authored-by: RhoMan¢er <github.pentagon482@passmail.net> Co-authored-by: Letta Code <noreply@letta.com>
This commit is contained in:
Harry Cliff
@@ -0,0 +1 @@
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* text=auto eol=lf
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+368
-12
@@ -929,6 +929,19 @@ pub fn rank_models_by_fit_opts_col(
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/// - ggerganov, llama.cpp Apple Silicon benchmarks (Discussion #4167)
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/// - Google, "Efficiently Scaling Transformer Inference" (arXiv:2211.05102)
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/// - ggerganov, llama.cpp NVIDIA T4 benchmarks (Discussion #4225)
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/// Read the system DDR bandwidth (GB/s) from the `LLMFIT_DDR_BANDWIDTH` env var,
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/// falling back to a conservative 50 GB/s default (DDR4-3200 dual-channel).
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///
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/// Override: `export LLMFIT_DDR_BANDWIDTH=90` for DDR5-5600 dual-channel, etc.
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/// Typical values: DDR4-3200 dual-channel ~50 GB/s, DDR5-5600 dual-channel ~90 GB/s.
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fn ddr_bandwidth_gbps() -> f64 {
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std::env::var("LLMFIT_DDR_BANDWIDTH")
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.ok()
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.and_then(|v| v.parse::<f64>().ok())
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.unwrap_or(50.0)
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}
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fn estimate_tps(
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model: &LlmModel,
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quant: &str,
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@@ -973,12 +986,80 @@ fn estimate_tps(
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&& let Some(bw) = bandwidth
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{
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let bytes_per_param = models::quant_bytes_per_param(quant);
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let model_gb = params * bytes_per_param;
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let active_gb = params * bytes_per_param;
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// Efficiency factor — captures overhead not in the simple
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// bandwidth / model-size formula. Tunable via CalcConfig.
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let efficiency = config.efficiency;
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let raw_tps = (bw / model_gb) * efficiency;
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if matches!(run_mode, RunMode::MoeOffload | RunMode::Gpu) && model.is_moe {
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// MoE expert speed estimation: the per-token cost is dominated by
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// reading the active expert weights, not GPU compute.
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//
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// Two scenarios:
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//
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// 1. MoeOffload mode: inactive experts in RAM, CPU reads active experts
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// from DDR memory -> DDR bandwidth is the bottleneck.
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// Model: expert_read_time = active_gb / ddr_bandwidth
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//
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// 2. GPU mode (model fits VRAM): most runtimes (Ollama, basic llama.cpp)
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// don't do expert-aware VRAM placement — they load all layers uniformly
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// and process the full model size per token, not just active experts.
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// Even runtimes that do expert-aware loading still read all expert weights
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// from VRAM on each token (just the active ones per layer), but the
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// VRAM bandwidth must cover the full model working set due to cache pressure
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// from 128+ experts.
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// Model: bandwidth / full_model_gb * efficiency (same as dense model)
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//
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// Measured examples on RX 6900 XT (16 GB VRAM, 512 GB/s, DDR4 ~50 GB/s):
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// - Qwen3-Next-80B (MoeOffload): estimated 15.2, measured 15.4
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// - Qwen3-30B-A3B (GPU mode, full-model): estimated 18.1, measured 16.3
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//
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// Note: PCIe bandwidth (~25 GB/s for Gen4 x16) could be the actual
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// ceiling on some systems, but in practice llama.cpp processes
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// offloaded layers on the CPU, so DDR bandwidth is the dominant factor.
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//
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// Typical DDR bandwidths: DDR4-3200 dual-channel ~50 GB/s,
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// DDR5-5600 dual-channel ~90 GB/s. We use a conservative 50 GB/s
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// default which can be overridden via LLMFIT_DDR_BANDWIDTH env var.
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if run_mode == RunMode::MoeOffload {
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let ddr_bw = ddr_bandwidth_gbps();
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let expert_read_time = active_gb / ddr_bw; // CPU reads from DDR
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let gpu_compute_time = active_gb / (bw * efficiency);
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let total_time = expert_read_time + gpu_compute_time;
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return (1.0 / total_time).max(0.1);
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}
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// GPU mode: MoE model fits in VRAM with ALL expert weights loaded.
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// The bandwidth bottleneck is reading the full model (all experts, not
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// just active ones) from VRAM. Use estimate_disk_gb which uses total
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// parameters (not active parameters) to get the actual model size that
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// must be transferred per token.
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//
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// MoE models suffer from irregular memory access patterns causing cache
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// thrashing. The overhead scales with expert count: negligible below 16
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// experts, significant at 128+. Calibrated to Qwen3-30B-A3B (128 experts,
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// 0.65 factor on RX 6900 XT).
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//
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// Measured on RX 6900 XT (512 GB/s, DDR4):
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// - Qwen3-30B-A3B Q2_K: estimated 16.2, measured 16.3
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let full_model_gb = model.estimate_disk_gb(quant);
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let moe_overhead = match model.num_experts {
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Some(n) if n <= 8 => 0.95, // Mixtral 8x7B — negligible overhead
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Some(n) if n <= 16 => 0.90,
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Some(n) if n <= 32 => 0.82,
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Some(n) if n <= 64 => 0.73,
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Some(_) => 0.65, // 128+ experts — full penalty
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None => 0.80, // unknown expert count — conservative
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};
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let raw_tps = (bw / full_model_gb) * efficiency * moe_overhead;
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let mode_factor = config.run_mode_factors.for_run_mode(run_mode);
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return (raw_tps * mode_factor).max(0.1);
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}
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let raw_tps = (bw / active_gb) * efficiency;
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let mode_factor = config.run_mode_factors.for_run_mode(run_mode);
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@@ -1011,6 +1092,25 @@ fn estimate_tps(
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base *= 1.1;
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}
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// MoE offload: apply the same DDR bandwidth bottleneck model as the
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// bandwidth-based path, estimating GPU bandwidth from the K constant.
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// K = bandwidth * efficiency / bytes_per_param
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// COUPLING: efficiency factor must match CalcConfig default (0.55)
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let fallback_efficiency = 0.55;
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if run_mode == RunMode::MoeOffload {
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let estimated_gpu_bw = k * models::quant_bytes_per_param(quant) / fallback_efficiency;
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let bytes_per_param = models::quant_bytes_per_param(quant);
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let active_gb = params * bytes_per_param;
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let ddr_bw = ddr_bandwidth_gbps();
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let expert_read_time = active_gb / ddr_bw;
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let gpu_compute_time = active_gb / (estimated_gpu_bw * fallback_efficiency);
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base = (1.0 / (expert_read_time + gpu_compute_time)).max(0.1);
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if system.total_cpu_cores >= 8 {
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base *= 1.1;
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}
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return base;
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}
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// CPU-only should use CPU K regardless of detected GPU
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if run_mode == RunMode::CpuOnly {
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let cpu_k = if cfg!(target_arch = "aarch64") {
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@@ -1738,14 +1838,6 @@ mod tests {
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InferenceRuntime::LlamaCpp,
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&test_config(),
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);
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let tps_moe = estimate_tps(
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&model,
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"Q4_K_M",
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&system,
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RunMode::MoeOffload,
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InferenceRuntime::LlamaCpp,
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&test_config(),
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);
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let tps_offload = estimate_tps(
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&model,
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"Q4_K_M",
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@@ -1764,8 +1856,7 @@ mod tests {
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);
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// GPU should be fastest
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assert!(tps_gpu > tps_moe);
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assert!(tps_moe > tps_offload);
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assert!(tps_gpu > tps_offload);
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assert!(tps_offload > tps_cpu);
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// All should be positive
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@@ -2135,4 +2226,269 @@ mod tests {
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let v100_sys = test_system_with_gpu(64.0, 16.0, "Tesla V100-PCIE-16GB");
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assert!(backend_compatible(&model, &v100_sys));
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}
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// ────────────────────────────────────────────────────────────────────
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// MoE offload DDR bandwidth speed estimation tests
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// ────────────────────────────────────────────────────────────────────
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/// Helper: create an MoE model with realistic expert parameters.
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fn test_moe_model(active_params_b: f64) -> LlmModel {
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LlmModel {
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name: "Test MoE".to_string(),
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provider: "Test".to_string(),
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parameter_count: "80B".to_string(),
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parameters_raw: Some(81_300_000_000),
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min_ram_gb: 45.0,
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recommended_ram_gb: 75.0,
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min_vram_gb: Some(42.0),
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quantization: "Q4_K_M".to_string(),
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context_length: 4096,
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use_case: "Chat".to_string(),
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is_moe: true,
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num_experts: Some(512),
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active_experts: Some(10),
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active_parameters: Some((active_params_b * 1_000_000_000.0) as u64),
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release_date: None,
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gguf_sources: vec![],
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capabilities: vec![],
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format: models::ModelFormat::default(),
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num_attention_heads: None,
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num_key_value_heads: None,
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num_hidden_layers: None,
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head_dim: None,
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attention_layout: None,
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license: None,
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}
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}
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#[test]
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fn test_moe_gpu_mode_uses_full_model_size() {
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// MoE models in GPU mode (fitting entirely in VRAM) should estimate
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// speed based on full model size, not just active params. This is because
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// runtimes don't do expert-aware VRAM placement — they process the full
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// model weights per token.
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let model = test_moe_model(3.3);
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let system = test_system_with_gpu(64.0, 16.0, "NVIDIA GeForce RTX 4090");
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let tps_gpu = estimate_tps(
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&model,
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"Q4_K_M",
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&system,
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RunMode::Gpu,
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InferenceRuntime::LlamaCpp,
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&test_config(),
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);
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let tps_moe = estimate_tps(
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&model,
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"Q4_K_M",
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&system,
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RunMode::MoeOffload,
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InferenceRuntime::LlamaCpp,
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&test_config(),
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);
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// Both modes should produce positive values
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assert!(tps_gpu > 0.0);
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assert!(tps_moe > 0.0);
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// GPU mode should use full model bandwidth (81.3B * 0.5bpp = 40.65 GB)
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// giving ~6.9 tok/s on RTX 4090 (1008 GB/s), NOT active-only (337+ tok/s)
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assert!(
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tps_gpu < 20.0,
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"GPU MoE mode should be realistic, got {tps_gpu:.1} tok/s (expected <20)"
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);
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// MoE offload uses active params only with DDR bottleneck
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// giving ~27 tok/s (3.3B active * 0.5bpp = 1.65 GB, DDR 50 GB/s)
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assert!(
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tps_moe > 10.0,
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"MoE offload should be reasonable, got {tps_moe:.1} tok/s"
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);
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}
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#[test]
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fn test_moe_offload_realistic_speed_rx6900xt() {
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// Validated against real-world measurement:
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// Qwen3-Next-80B (3.3B active params) on RX 6900 XT (16 GB VRAM)
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// with llama.cpp MoE splitting -> 15.4 tok/s measured
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let model = test_moe_model(3.3);
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let system = test_system_with_gpu(64.0, 16.0, "AMD Radeon RX 6900 XT");
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let tps = estimate_tps(
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&model,
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"Q4_K_M",
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&system,
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RunMode::MoeOffload,
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InferenceRuntime::LlamaCpp,
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&test_config(),
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);
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// Must NOT be 80+ tok/s (old broken estimate)
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assert!(
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tps < 30.0,
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"MoE offload estimate should be realistic, got {tps:.1} tok/s (old bug was ~80)"
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);
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// Must be positive and reasonable
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assert!(
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tps > 5.0,
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"MoE offload should still produce usable estimates, got {tps:.1} tok/s"
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);
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}
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#[test]
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fn test_moe_offload_faster_on_older_gpu_with_slower_vram() {
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// Slower GPU VRAM shouldn't matter much for MoE offload since
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// the bottleneck is DDR bandwidth, not GPU bandwidth.
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let model = test_moe_model(3.3);
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let sys_fast_gpu = test_system_with_gpu(64.0, 16.0, "NVIDIA GeForce RTX 4090");
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let sys_slow_gpu = test_system_with_gpu(64.0, 16.0, "Tesla T4");
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let tps_fast = estimate_tps(
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&model,
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"Q4_K_M",
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&sys_fast_gpu,
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RunMode::MoeOffload,
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InferenceRuntime::LlamaCpp,
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&test_config(),
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);
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let tps_slow = estimate_tps(
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&model,
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"Q4_K_M",
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&sys_slow_gpu,
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RunMode::MoeOffload,
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InferenceRuntime::LlamaCpp,
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&test_config(),
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);
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let ratio = tps_fast / tps_slow;
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assert!(
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ratio < 1.5,
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"MoE offload should NOT scale strongly with GPU bandwidth: fast={tps_fast:.1}, slow={tps_slow:.1}, ratio={ratio:.2}"
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);
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}
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#[test]
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fn test_moe_offload_gpu_mode_does_scale_with_gpu_bandwidth() {
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// Contrast: full GPU mode SHOULD scale strongly with GPU bandwidth
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let model = test_moe_model(3.3);
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let sys_fast_gpu = test_system_with_gpu(64.0, 24.0, "NVIDIA GeForce RTX 4090");
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let sys_slow_gpu = test_system_with_gpu(64.0, 16.0, "Tesla T4");
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let tps_fast = estimate_tps(
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&model,
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"Q4_K_M",
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&sys_fast_gpu,
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RunMode::Gpu,
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InferenceRuntime::LlamaCpp,
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&test_config(),
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);
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let tps_slow = estimate_tps(
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&model,
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"Q4_K_M",
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&sys_slow_gpu,
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RunMode::Gpu,
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InferenceRuntime::LlamaCpp,
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&test_config(),
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);
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let ratio = tps_fast / tps_slow;
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assert!(
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ratio > 2.0,
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"Full GPU mode SHOULD scale with GPU bandwidth: fast={tps_fast:.1}, slow={tps_slow:.1}, ratio={ratio:.2}"
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);
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}
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#[test]
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fn test_moe_offload_increases_with_smaller_active_params() {
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let model_small = test_moe_model(1.5);
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let model_large = test_moe_model(6.0);
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let system = test_system_with_gpu(64.0, 16.0, "NVIDIA GeForce RTX 4090");
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let tps_small = estimate_tps(
|
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&model_small,
|
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"Q4_K_M",
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&system,
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RunMode::MoeOffload,
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InferenceRuntime::LlamaCpp,
|
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&test_config(),
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);
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let tps_large = estimate_tps(
|
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&model_large,
|
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"Q4_K_M",
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&system,
|
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RunMode::MoeOffload,
|
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InferenceRuntime::LlamaCpp,
|
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&test_config(),
|
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);
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assert!(
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tps_small > tps_large,
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"Smaller active params should be faster: small={tps_small:.1}, large={tps_large:.1}"
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);
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}
|
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|
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#[test]
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fn test_moe_offload_must_use_active_params_not_total() {
|
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let model = test_moe_model(3.3);
|
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let system = test_system_with_gpu(64.0, 16.0, "NVIDIA GeForce RTX 4090");
|
||||
|
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let tps = estimate_tps(
|
||||
&model,
|
||||
"Q4_K_M",
|
||||
&system,
|
||||
RunMode::MoeOffload,
|
||||
InferenceRuntime::LlamaCpp,
|
||||
&test_config(),
|
||||
);
|
||||
|
||||
assert!(
|
||||
tps > 5.0,
|
||||
"MoE offload should use active params: got {tps:.1} (would be ~2 if using total params)"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_moe_offload_positive_for_unknown_gpu() {
|
||||
let model = test_moe_model(3.3);
|
||||
let system = test_system_with_gpu(64.0, 16.0, "Unknown GPU");
|
||||
|
||||
let tps = estimate_tps(
|
||||
&model,
|
||||
"Q4_K_M",
|
||||
&system,
|
||||
RunMode::MoeOffload,
|
||||
InferenceRuntime::LlamaCpp,
|
||||
&test_config(),
|
||||
);
|
||||
|
||||
assert!(
|
||||
tps > 0.0,
|
||||
"MoE offload fallback should produce positive estimate"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_moe_offload_analyze_matches_estimate_tps() {
|
||||
let model = test_moe_model(3.3);
|
||||
// Small VRAM to force MoE offload path
|
||||
let system = test_system_with_gpu(64.0, 8.0, "NVIDIA GeForce RTX 4090");
|
||||
|
||||
let fit = ModelFit::analyze(&model, &system);
|
||||
|
||||
assert!(
|
||||
matches!(fit.run_mode, RunMode::MoeOffload),
|
||||
"Expected MoEOffload, got {:?}",
|
||||
fit.run_mode
|
||||
);
|
||||
|
||||
assert!(
|
||||
fit.estimated_tps < 30.0,
|
||||
"analyze() should produce realistic MoE speed, got {:.1}",
|
||||
fit.estimated_tps
|
||||
);
|
||||
assert!(
|
||||
fit.estimated_tps > 0.0,
|
||||
"analyze() should produce positive MoE speed"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user