vllm-performance-tuning

# vLLM performance tuning

Target: operators deploying models on new hardware, chasing throughput / latency / goodput SLOs, or diagnosing perf regressions. Current through v0.21.0 stable (2026-05-15); v0.20.x stable since 2026-04-27. Last freshened 2026-05-28.

Companion skills: `vllm-benchmarking` (measure), `vllm-caching` (KV), `vllm-nvidia-hardware` (GPU/GEMM), `vllm-configuration` (env vars), `vllm-observability` (metrics).

## Tuning levers (apply by goal, not in fixed order)

**Always first — characterize the workload.** ISL / OSL / req/s / concurrency / SLO (P95 TTFT, P95 TPOT, P95 ITL). "Goodput" = tok/s/GPU **under SLO**, not raw tok/s. Everything below is keyed off these numbers.

**Parallelism + MoE kernels (biggest single wins):**
- **Pick parallelism** (see `references/moe-and-ep.md`) — model-fits-1-GPU → TP=1 + replicas (DP); MoE MLA (DeepSeek/Kimi-K2) → DP-attn + EP; multi-node → TP intra + PP inter OR Wide-EP.
- **MoE on a new SKU → run `benchmark_moe.py --tune`** — generates `E=*,N=*,device_name=*.json` configs. Without tuned configs vLLM logs "Using default MoE config. Performance might be sub-optimal!" = 20-40% throughput loss.
- **Wide-EP** (`--enable-expert-parallel --enable-eplb --enable-dbo`) for DeepSeek/Qwen3/Kimi-K2 at ≥16 GPUs.

**Throughput / batching:**
- **`auto_tune.sh`** (`benchmarks/auto_tune/`) sweeps `max_num_seqs × max_num_batched_tokens`.
- **`--gpu-memory-utilization`** — raise from 0.90 toward 0.95 until steady OOM margin, then back off. MoE: cap at 0.85 (all-to-all buffers not in accounting).
- **Chunked prefill (always on in V1)** — raise `--max-num-batched-tokens` (default 2048 since PR #10544) if TTFT > SLO; lower if ITL > SLO.

**Latency / graph + compile:**
- **CUDA graphs** — keep `FULL_AND_PIECEWISE` (default); align `--cuda-graph-sizes` with `max_num_seqs*2`.
- **`--async-scheduling`** — default-on in recent releases unless using spec-dec / PP / unsupported MM path.
- **Compile cache** — pre-bake `$VLLM_CACHE_ROOT/torch_compile_cache` on a representative pod; mount as PVC / bake into OCI layer.

**Distributed (last resort, only when the lever above is exhausted):**
- **NCCL** — on well-configured clouds do nothing. Bare-metal IB: `NCCL_IB_HCA`, `NCCL_IB_GID_INDEX`, `NCCL_NET_GDR_LEVEL`. Never `NCCL_CUMEM_ENABLE=0` on GB200.
- **PD disagg** — reach for only after the above are exhausted and prefill interference is the actual bottleneck.

## Triage tree ("why is it slow")

From Red Hat's 5-step triage ([2026-03-09](https://developers.redhat.com/articles/2026/03/09/5-steps-triage-vllm-performance)):

| Symptom | Look at | Common cause |
|---|---|---|
| TTFT high, queue empty | compute-bound prefill | chunked-prefill budget too low, no prefix cache, bad parallelism |
| TTFT high, queue growing | capacity | raise replicas, raise `max_num_seqs`, check preemption rate |
| TPOT high, TTFT fine | decode-bound | MoE kernel not tuned, wrong attention backend, async sched off |
| ITL spikes | CUDA-graph miss | batch sizes fall outside captured buckets |
| Preemptions climbing | KV thrashing | raise `--swap-space`, lower `--max-num-seqs`, or add replicas |
| `num_running` < configured concurrency | scheduler stall | check async-sched blockers, multimodal path, structured output |

**DCGM signals** (not `GPU_UTIL`): `DCGM_FI_PROF_SM_OCCUPANCY`, `DCGM_FI_PROF_PIPE_TENSOR_ACTIVE`. Low tensor-core active on a GEMM-bound workload = memory-bound.

## Quick-answer router

**MoE tuning + expert parallelism + DeepEP + EPLB + parallelism decision matrix** → `references/moe-and-ep.md`

**Scheduler knobs + CUDA graphs + torch.compile + compile cache** → `references/scheduler-and-compile.md`

**NCCL / InfiniBand / DCGM + PD disaggregation (Nixl/Mooncake/LMCache)** → `references/distributed.md`

**Known regressions + vendor quirks (AMD / Ascend / XPU)** → `references/regressions.md`

**Full citation anchors** → `references/sources.md`

## Top 10 operator mistakes this skill exists to prevent

1. **Not running `benchmark_moe.py` on a new GPU SKU.** Shipped configs cover common combos (H100, H200, A100, MI300X for Mixtral). B200 / B300 / GB200 / MI325X / Jetson Thor / RTX Pro Blackwell almost always need re-tuning. Symptom: `Using default MoE config. Performance might be sub-optimal!` in logs. Fix: `python benchmarks/kernels/benchmark_moe.py --model <moe-model> --tp-size <N> --enable-expert-parallel --tune --save-dir ./configs`, then `export VLLM_TUNED_CONFIG_FOLDER=./configs`. Expected: 20-40% throughput recovery.

2. **`gpu_memory_utilization=0.95` on MoE.** All-to-all staging buffers (DeepEP, NVSHMEM) aren't in the memory accountant. OOM at high concurrency. Fix: cap at 0.85 for MoE, 0.92 for dense.

3. **TP not divisible by head count.** Model has 32 heads, TP=7 → shape mismatch. Rule: `num_heads % TP == 0` AND `hidden_size % TP == 0`.

4. **DeepSeek-V3.2 at TP=8 on H100/H200/B200/B300.** FlashMLA-Sparse only uses 16 heads per rank, padded to 64 → overhead. Fix: `DP=8, EP=8, TP=1`. ([DeepSeek-V3.2 recipe](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html))

5. **Llama-4-Maverick with `--enable-expert-parallel`.** Activation density 0.78% (1/128) — AllToAll overhead exceeds parallelism win. **EP hurts 7-12%** vs TP-only for Maverick. DeepSeek-R1 (3.13%) and Qwen3-235B (6.25%) benefit from EP. Rule: only enable EP when `(experts_per_token / total_experts) > 2%`.

6. **MLA model with TP=8.** Single KV head, TP duplicates ~84.5 GB KV cache per rank. Fix: DP-attention + EP-MoE splits KV to ~0.125 GB/GPU/request.

7. **`--async-scheduling` with unsupported path.** Structured outputs (fixed #26866), spec-dec (#24799 fixed), PP/struct-out/spec-dec/MM umbrella tracker #27679 closed 2025-12-29 — all sub-PRs merged. The vllm-ascend v0.11.0rc2 precision bug (#4649) is fixed — **closed 2026-03-13**; upgrade rather than disabling async-sched. Symptom: stall, latency regression, or precision loss. Fix: upgrade; only disable if you've reproduced the issue on your version.

8. **Shipping `VLLM_MOE_USE_DEEP_GEMM=1` blindly on H200.** Between `d83f3f7` and `5a84b76` the DeepGEMM MoE M<128 restriction was removed; H200 DeepSeek-R1 EP at concurrency ≤8 regressed 1.5× TTFT. **[#28882](https://github.com/vllm-project/vllm/issues/28882) closed 2026-04-21** — upgrade to v0.19.1+ or current main. For pre-v0.19.1 deployments, the workaround was `VLLM_MOE_USE_DEEP_GEMM=0` + FlashInfer FP8 for low-concurrency decode; re-benchmark after upgrading before removing the override.

9. **Skipping compile cache on K8s.** First-pod torch.compile = 5-15 min on large models. Fix: pre-bake `$VLLM_CACHE_ROOT/torch_compile_cache` on one pod, mount as PVC / OCI layer. Llama-4 specifically needs `VLLM_DISABLE_COMPILE_CACHE=1` — stale-cache bug.

10. **`NCCL_CUMEM_ENABLE=0` on GB200.** Disables multi-node NVLink, forces TCP/IB fallback. Nvidia's rule: **"users should not need to tune NCCL environment variables"** on modern clouds. GB200 set `NCCL_NET_GDR_C2C=1`. PR [#16992](https://github.com/vllm-project/vllm/pull/16992) fixed vLLM's defaults.

## Operator cheat sheet

### MoE tune on new hardware (canonical recipe)

```bash
# Step 1 — run the tuner (uses Ray to parallelize across local GPUs)
python benchmarks/kernels/benchmark_moe.py \
  --model deepseek-ai/DeepSeek-V3 \
  --tp-size 8 --enable-expert-parallel --dtype fp8_w8a8 \
  --tune --save-dir ./moe_configs

# Step 2 — point vLLM at the configs
export VLLM_TUNED_CONFIG_FOLDER=./moe_configs

# Step 3 — serve + verify no "default MoE config" warning in logs
vllm serve deepseek-ai/DeepSeek-V3 --tensor-parallel-size 8 \
  --enable-expert-parallel --enable-eplb --enable-dbo \
  --gpu-memory-utilization 0.85
```

### Parallelism first-pick table

| Model family | Small-scale | Large-scale (≥16 GPUs) |
|---|---|---|
| Dense (Llama, Qwen3-dense) | TP=N, DP=replicas | TP=8 intra-node + PP=nodes OR TP=8 + DP=N |
| MoE non-MLA (Mixtral, Qwen3-MoE) | TP=N, EP off | TP + EP: EP = E / TP |
| MoE MLA (DeepSeek-V3/R1, Kimi-K2) | DP=N + EP | Wide-EP: DP-attn + EP-MoE, `--enable-eplb` |
| Llama-4-Maverick (0.78% density) | TP only | TP only (EP hurts) |
| DeepSeek-V3.2 (FlashMLA-Sparse) | DP=8, EP=8, TP=1 | same |

Concurrency crossover (8× MI300X benchmarks): ≤128 concurrent → TP wins, ≥512 → DP wins, 256-512 mixed. ([AMD MoE playbook](https://rocm.blogs.amd.com/software-tools-optimization/vllm-moe-guide/README.html))

### Scheduler first-pass by workload

| Scenario | `max_num_batched_tokens` | `max_num_seqs` | Other |
|---|---|---|---|
| Throughput-heavy (batch decode) | 4096-16384 | 256-512 | async sched on |
| Latency-heavy (chat) | 1024-2048 | 64-128 | async sched on, `--stream-interval 1` |
| Long-context RAG | 8192-16384 | 32-64 | `--enable-prefix-caching`, `--long-prefill-token-threshold` |
| Wide-EP DeepSeek | 8192 | 256 | `--enable-expert-parallel --enable-eplb --enable-dbo`, `FULL_AND_PIECEWISE` |

### Compile-level shorthand

| Flag | Effect |
|---|---|
| `-O0` | No compile, no CUDA graphs (= `--enforce-eager`) |
| `-O1` | Simple compile + PIECEWISE graphs |
| **`-O2`** | **default** — full compile + `FULL_AND_PIECEWISE` + fusions (AllReduce+RMSNorm +15%, SP+Async-TP +10%, Attention+Quant FP8 +7%) |
| `-O3` | reserved (currently = `-O2`) |

### Key numbers to memorize

| Metric | Value |
|---|---|
| Default `max_num_batched_tokens` (since PR #10544) | 2048 (was 512) |
| Default `max_num_seqs` | 256 |
| Default CUDA-graph sizes | `[1,2,4] + range(8,256,8) + range(256,max,16)`, cap `min(max_num_seqs*2, 512)` |
| H200 Wide-EP DeepSeek-R1 throughput | 2.2k tok/s/GPU vs ~1.5k baseline ([vllm.ai/blog/large-scale-serving](https://vllm.ai/blog/large-scale-serving)) |
| GB200 Wide-EP DeepSeek-R1 | 26.2K TPGS prefill, 10.1K TPGS decode, 3-5× H200 ([vllm.ai/blog/dsr1-gb200-part1](https://vllm.ai/blog/dsr1-gb200-part1)) |
| MLPerf v5.1 Blackwell Ultra | 5,842 tok/s/GPU offline, 2,907 server ([NVIDIA blog](https://developer.nvidia.com/blog/nvidia-blackwell-ultra-sets-new-inference-records-in-mlperf-debut/)) |
| DeepEP dispatch (FP8, Azure H100 IB400) | 45.9 GB/s RDMA, 149.8 GB/s NVLink ([Azure blog](https://techcommunity.microsoft.com/blog/azurehighperformancecomputingblog/achieving-optimal-performance-for-deepseek-expert-parallelism-deepep-on-azure/4414699)) |
| Activation density cutoff for EP win | > 2% (below: TP wins) |

## Source policy

All claims cite file:line, release-note PR refs, or issue IDs. Full anchor list + vendor-specific sources in `references/sources.md`. Compiled 2026-04-18 against v0.19.0; freshened 2026-05-28 against v0.21.0 stable (#28882 closed 2026-04-21, #27679 closed 2025-12-29, #31679 closed 2026-01-07, #34641 closed 2026-05-28, ascend #4649 closed 2026-03-13). Next refresh when v0.22.x stable ships or when remaining key regressions (#35048, #31475) close.