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Add DeepSeek-V4-Pro FP4 B200 Dynamo TensorRT-LLM recipes / 添加 DeepSeek-V4-Pro FP4 B200 Dynamo TensorRT-LLM 配置 #2552

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Add DeepSeek-V4-Pro FP4 B200 Dynamo TensorRT-LLM recipes / 添加 DeepSeek-V4-Pro FP4 B200 Dynamo TensorRT-LLM 配置 #2552

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229334b
feat(config): add DSV4 FP4 B200 Dynamo TRT recipes
RohitNagraj Aug 10, 2026
84ff0cd
chore(changelog): add PR 2552 link
RohitNagraj Aug 10, 2026
193c2d8
chore: merge main and address review feedback
RohitNagraj Aug 14, 2026
248d409
Update nvidia-master.yaml
cquil11 Aug 14, 2026
67dfbba
Merge branch 'main' into dsv4-fp4-b200-dynamo-trt
cquil11 Aug 14, 2026
c43a5c9
Update perf-changelog.yaml
cquil11 Aug 14, 2026
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65 changes: 65 additions & 0 deletions benchmarks/multi_node/srt-slurm-recipes/configs/moe_load_balancer_ctx_ep4_384.yaml
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65 changes: 65 additions & 0 deletions benchmarks/multi_node/srt-slurm-recipes/configs/moe_load_balancer_gen_ep8_slots384.yaml
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123 changes: 123 additions & 0 deletions ...recipes/trtllm/deepseek-v4/8k1k/b200/disagg-b200-8k1k-1p2d-dep8-dep8-b4-eplb384-mtp3.yaml
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name: disagg-b200-8k1k-1p2d-dep8-dep8-b4-eplb384-mtp3
model:
path: deepseek-v4-pro
container: nvcr.io#nvidia/tensorrt-llm/release:1.3.0rc23
precision: fp4
dynamo:
install: true
wheel: "1.4.0.dev20260730"
request_plane: tcp
resources:
gpu_type: b200
prefill_nodes: 1
prefill_workers: 1
gpus_per_prefill: 8
decode_nodes: 2
decode_workers: 2
gpus_per_decode: 8
gpus_per_node: 8
backend:
type: trtllm
prefill_environment:
TLLM_LOG_LEVEL: INFO
TRTLLM_SERVER_DISABLE_GC: '1'
TRTLLM_WORKER_DISABLE_GC: '1'
TRTLLM_ENABLE_PDL: '1'
ENROOT_ALLOW_DEV: 'yes'
NCCL_GRAPH_MIXING_SUPPORT: '0'
MIMALLOC_PURGE_DELAY: '0'
PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True
TLLM_PROFILE_LOG_RANKS: all
TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS: '1.82'
UCX_TLS: rc,cuda_ipc,cuda_copy,sm,self,tcp
NIXL_PLUGIN_DIR: /opt/nvidia/nvda_nixl/lib/x86_64-linux-gnu/plugins
decode_environment:
TLLM_LOG_LEVEL: INFO
TRTLLM_SERVER_DISABLE_GC: '1'
TRTLLM_WORKER_DISABLE_GC: '1'
TRTLLM_ENABLE_PDL: '1'
ENROOT_ALLOW_DEV: 'yes'
NCCL_GRAPH_MIXING_SUPPORT: '0'
MIMALLOC_PURGE_DELAY: '0'
TLLM_PROFILE_LOG_RANKS: all
TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS: '1.82'
UCX_TLS: rc,cuda_ipc,cuda_copy,sm,self,tcp
NIXL_PLUGIN_DIR: /opt/nvidia/nvda_nixl/lib/x86_64-linux-gnu/plugins
trtllm_config:
prefill:
cache_transceiver_config:
backend: NIXL
kv_transfer_timeout_ms: 600000
transceiver_runtime: PYTHON
cuda_graph_config:
disable_overlap_scheduler: true
enable_attention_dp: true
kv_cache_config:
dtype: fp8
enable_block_reuse: false
free_gpu_memory_fraction: 0.5
tokens_per_block: 128
max_batch_size: 2
max_num_tokens: 8192
max_seq_len: 8232
moe_config:
backend: TRTLLM
load_balancer: /configs/moe_load_balancer_ctx_ep4_384.yaml
moe_expert_parallel_size: 8
pipeline_parallel_size: 1
print_iter_log: true
speculative_config:
decoding_type: MTP
max_draft_len: 3
tensor_parallel_size: 8
decode:
cache_transceiver_config:
backend: NIXL
kv_transfer_timeout_ms: 600000
transceiver_runtime: PYTHON
cuda_graph_config:
batch_sizes:
- 1
- 2
- 4
enable_padding: true
enable_attention_dp: true
enable_lm_head_tp_in_adp: true
kv_cache_config:
dtype: fp8
enable_block_reuse: false
free_gpu_memory_fraction: 0.8
tokens_per_block: 128
max_batch_size: 4
max_num_tokens: 16
max_seq_len: 9256
moe_config:
backend: TRTLLM
load_balancer: /configs/moe_load_balancer_gen_ep8_slots384.yaml
use_low_precision_moe_combine: true
moe_expert_parallel_size: 8
num_postprocess_workers: 4
pipeline_parallel_size: 1
print_iter_log: true
speculative_config:
decoding_type: MTP
max_draft_len: 3
stream_interval: 100
tensor_parallel_size: 8
benchmark:
type: sa-bench
isl: 8192
osl: 1024
concurrencies: "64"
req_rate: inf
random_range_ratio: 0.8
use_chat_template: true
custom_tokenizer: "sa_bench_tokenizers.sglang_deepseek_v4.SGLangDeepseekV4Tokenizer"
frontend:
type: dynamo
enable_multiple_frontends: false
health_check:
max_attempts: 540
interval_seconds: 10
environment:
TRTLLM_SERVER_DISABLE_GC: '1'
116 changes: 116 additions & 0 deletions ...recipes/trtllm/deepseek-v4/8k1k/b200/disagg-b200-8k1k-1p3d-dep8-dep8-b8-eplb384-mtp0.yaml
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name: disagg-b200-8k1k-1p3d-dep8-dep8-b8-eplb384-mtp0
model:
path: deepseek-v4-pro
container: nvcr.io#nvidia/tensorrt-llm/release:1.3.0rc23
precision: fp4
dynamo:
install: true
wheel: "1.4.0.dev20260730"
request_plane: tcp
resources:
gpu_type: b200
prefill_nodes: 1
prefill_workers: 1
gpus_per_prefill: 8
decode_nodes: 3
decode_workers: 3
gpus_per_decode: 8
gpus_per_node: 8
backend:
type: trtllm
prefill_environment:
TLLM_LOG_LEVEL: INFO
TRTLLM_SERVER_DISABLE_GC: '1'
TRTLLM_WORKER_DISABLE_GC: '1'
TRTLLM_ENABLE_PDL: '1'
ENROOT_ALLOW_DEV: 'yes'
NCCL_GRAPH_MIXING_SUPPORT: '0'
MIMALLOC_PURGE_DELAY: '0'
PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True
TLLM_PROFILE_LOG_RANKS: all
UCX_TLS: rc,cuda_ipc,cuda_copy,sm,self,tcp
NIXL_PLUGIN_DIR: /opt/nvidia/nvda_nixl/lib/x86_64-linux-gnu/plugins
decode_environment:
TLLM_LOG_LEVEL: INFO
TRTLLM_SERVER_DISABLE_GC: '1'
TRTLLM_WORKER_DISABLE_GC: '1'
TRTLLM_ENABLE_PDL: '1'
ENROOT_ALLOW_DEV: 'yes'
NCCL_GRAPH_MIXING_SUPPORT: '0'
MIMALLOC_PURGE_DELAY: '0'
TLLM_PROFILE_LOG_RANKS: all
UCX_TLS: rc,cuda_ipc,cuda_copy,sm,self,tcp
NIXL_PLUGIN_DIR: /opt/nvidia/nvda_nixl/lib/x86_64-linux-gnu/plugins
trtllm_config:
prefill:
cache_transceiver_config:
backend: NIXL
kv_transfer_timeout_ms: 600000
transceiver_runtime: PYTHON
cuda_graph_config:
disable_overlap_scheduler: true
enable_attention_dp: true
kv_cache_config:
dtype: fp8
enable_block_reuse: false
free_gpu_memory_fraction: 0.5
tokens_per_block: 128
max_batch_size: 2
max_num_tokens: 8192
max_seq_len: 8232
moe_config:
backend: TRTLLM
load_balancer: /configs/moe_load_balancer_ctx_ep4_384.yaml
moe_expert_parallel_size: 8
pipeline_parallel_size: 1
print_iter_log: true
tensor_parallel_size: 8
decode:
cache_transceiver_config:
backend: NIXL
kv_transfer_timeout_ms: 600000
transceiver_runtime: PYTHON
cuda_graph_config:
batch_sizes:
- 1
- 2
- 4
- 8
enable_padding: true
enable_attention_dp: true
enable_lm_head_tp_in_adp: false
kv_cache_config:
dtype: fp8
enable_block_reuse: false
free_gpu_memory_fraction: 0.8
tokens_per_block: 128
max_batch_size: 8
max_num_tokens: 8
max_seq_len: 9256
moe_config:
backend: TRTLLM
load_balancer: /configs/moe_load_balancer_gen_ep8_slots384.yaml
use_low_precision_moe_combine: true
moe_expert_parallel_size: 8
num_postprocess_workers: 4
pipeline_parallel_size: 1
print_iter_log: true
stream_interval: 100
tensor_parallel_size: 8
benchmark:
type: sa-bench
isl: 8192
osl: 1024
concurrencies: "192"
req_rate: inf
random_range_ratio: 0.8
use_chat_template: true
custom_tokenizer: "sa_bench_tokenizers.sglang_deepseek_v4.SGLangDeepseekV4Tokenizer"
frontend:
type: dynamo
enable_multiple_frontends: false
health_check:
max_attempts: 540
interval_seconds: 10
environment:
TRTLLM_SERVER_DISABLE_GC: '1'
123 changes: 123 additions & 0 deletions ...recipes/trtllm/deepseek-v4/8k1k/b200/disagg-b200-8k1k-1p4d-dep8-dep8-b2-eplb384-mtp3.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,123 @@
name: disagg-b200-8k1k-1p4d-dep8-dep8-b2-eplb384-mtp3
model:
path: deepseek-v4-pro
container: nvcr.io#nvidia/tensorrt-llm/release:1.3.0rc23
precision: fp4
dynamo:
install: true
wheel: "1.4.0.dev20260730"
request_plane: tcp
resources:
gpu_type: b200
prefill_nodes: 1
prefill_workers: 1
gpus_per_prefill: 8
decode_nodes: 4
decode_workers: 4
gpus_per_decode: 8
gpus_per_node: 8
backend:
type: trtllm
prefill_environment:
TLLM_LOG_LEVEL: INFO
TRTLLM_SERVER_DISABLE_GC: '1'
TRTLLM_WORKER_DISABLE_GC: '1'
TRTLLM_ENABLE_PDL: '1'
ENROOT_ALLOW_DEV: 'yes'
NCCL_GRAPH_MIXING_SUPPORT: '0'
MIMALLOC_PURGE_DELAY: '0'
PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True
TLLM_PROFILE_LOG_RANKS: all
TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS: '1.82'
UCX_TLS: rc,cuda_ipc,cuda_copy,sm,self,tcp
NIXL_PLUGIN_DIR: /opt/nvidia/nvda_nixl/lib/x86_64-linux-gnu/plugins
decode_environment:
TLLM_LOG_LEVEL: INFO
TRTLLM_SERVER_DISABLE_GC: '1'
TRTLLM_WORKER_DISABLE_GC: '1'
TRTLLM_ENABLE_PDL: '1'
ENROOT_ALLOW_DEV: 'yes'
NCCL_GRAPH_MIXING_SUPPORT: '0'
MIMALLOC_PURGE_DELAY: '0'
TLLM_PROFILE_LOG_RANKS: all
TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS: '1.82'
UCX_TLS: rc,cuda_ipc,cuda_copy,sm,self,tcp
NIXL_PLUGIN_DIR: /opt/nvidia/nvda_nixl/lib/x86_64-linux-gnu/plugins
trtllm_config:
prefill:
cache_transceiver_config:
backend: NIXL
kv_transfer_timeout_ms: 600000
transceiver_runtime: PYTHON
cuda_graph_config:
disable_overlap_scheduler: true
enable_attention_dp: true
kv_cache_config:
dtype: fp8
enable_block_reuse: false
free_gpu_memory_fraction: 0.5
tokens_per_block: 128
max_batch_size: 2
max_num_tokens: 8192
max_seq_len: 8232
moe_config:
backend: TRTLLM
load_balancer: /configs/moe_load_balancer_ctx_ep4_384.yaml
moe_expert_parallel_size: 8
pipeline_parallel_size: 1
print_iter_log: true
speculative_config:
decoding_type: MTP
max_draft_len: 3
tensor_parallel_size: 8
decode:
cache_transceiver_config:
backend: NIXL
kv_transfer_timeout_ms: 600000
transceiver_runtime: PYTHON
cuda_graph_config:
batch_sizes:
- 1
- 2
- 4
enable_padding: true
enable_attention_dp: true
enable_lm_head_tp_in_adp: true
kv_cache_config:
dtype: fp8
enable_block_reuse: false
free_gpu_memory_fraction: 0.8
tokens_per_block: 128
max_batch_size: 2
max_num_tokens: 8

Check warning on line 92 in benchmarks/multi_node/srt-slurm-recipes/trtllm/deepseek-v4/8k1k/b200/disagg-b200-8k1k-1p4d-dep8-dep8-b2-eplb384-mtp3.yaml

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Claude / Claude Code Review

decode cuda_graph batch_sizes exceeds max_batch_size in 1p4d-b2-eplb384-mtp3 recipe 

In `disagg-b200-8k1k-1p4d-dep8-dep8-b2-eplb384-mtp3.yaml`, decode's `cuda_graph_config.batch_sizes` is `[1, 2, 4]` but `decode.max_batch_size` is `2`, so the batch-4 CUDA graph can never be exercised. This looks like a copy-paste leftover from the sibling b4 recipe (`disagg-b200-8k1k-1p2d-dep8-dep8-b4-eplb384-mtp3.yaml`, which correctly uses `[1, 2, 4]` with `max_batch_size: 4`) — `batch_sizes` should be trimmed to `[1, 2]` here.
Comment on lines +75 to +90

@claude claude Bot Aug 10, 2026

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🟡 In disagg-b200-8k1k-1p4d-dep8-dep8-b2-eplb384-mtp3.yaml, decode's cuda_graph_config.batch_sizes is [1, 2, 4] but decode.max_batch_size is 2, so the batch-4 CUDA graph can never be exercised. This looks like a copy-paste leftover from the sibling b4 recipe (disagg-b200-8k1k-1p2d-dep8-dep8-b4-eplb384-mtp3.yaml, which correctly uses [1, 2, 4] with max_batch_size: 4) — batch_sizes should be trimmed to [1, 2] here.

Extended reasoning...

The bug: In benchmarks/multi_node/srt-slurm-recipes/trtllm/deepseek-v4/8k1k/b200/disagg-b200-8k1k-1p4d-dep8-dep8-b2-eplb384-mtp3.yaml, the decode block sets:

max_batch_size: 2
max_num_tokens: 8
cuda_graph_config:
  batch_sizes:
  - 1
  - 2
  - 4
  enable_padding: true

TensorRT-LLM's decode-side batching can never dispatch more than max_batch_size (2) requests at once, so the batch-size-4 CUDA graph entry is unreachable — no request stream can ever fill it. This is purely a config-consistency issue, not a crash: TRT-LLM either ignores/clamps cuda-graph batch sizes above max_batch_size or wastes one extra graph capture (time + a bit of GPU memory) for a size it will never use.

Why this happened: Every other recipe added in this PR keeps the largest cuda_graph_config.batch_sizes entry exactly equal to max_batch_size — e.g. the sibling disagg-b200-8k1k-1p2d-dep8-dep8-b4-eplb384-mtp3.yaml (same dep8-dep8/eplb384/mtp3 template, just b4 instead of b2) uses the identical list [1, 2, 4] but with max_batch_size: 4. The max_num_tokens: 8 in the b2 file (2 requests × 4 tokens/step under MTP draft_len=3) further corroborates that this file was derived from the b4 template by reducing max_batch_size/max_num_tokens from 4→2, but the batch_sizes list was never trimmed to match.

Step-by-step proof:

  1. Decode's scheduler is capped at max_batch_size: 2 — it will never admit a 3rd or 4th concurrent request.
  2. At startup, TensorRT-LLM captures a CUDA graph for each entry in batch_sizes: sizes 1, 2, and 4.
  3. Because the scheduler cap is 2, no runtime batch can ever reach size 4, so the size-4 graph is captured but never invoked.
  4. Cross-checking against all 16 recipes in this PR (b4, b8, b16, b32, b64, b128, b256 variants), every single one keeps max(batch_sizes) == max_batch_size; this b2 file is the sole outlier.

Fix: Change batch_sizes to [1, 2] to match max_batch_size: 2, consistent with every other recipe in this PR.

Why nit, not normal: This doesn't break the run — TensorRT-LLM handles cuda-graph batch sizes above the scheduler cap gracefully (filtering/clamping), so the worst-case impact is a modest amount of wasted graph-capture time and GPU memory at startup, not a functional failure or incorrect output. It's a legitimate cleanup that all three independent verifiers agreed should be fixed but flagged as non-blocking.

max_seq_len: 9256
moe_config:
backend: TRTLLM
load_balancer: /configs/moe_load_balancer_gen_ep8_slots384.yaml
use_low_precision_moe_combine: true
moe_expert_parallel_size: 8
num_postprocess_workers: 4
pipeline_parallel_size: 1
print_iter_log: true
speculative_config:
decoding_type: MTP
max_draft_len: 3
stream_interval: 100
tensor_parallel_size: 8
benchmark:
type: sa-bench
isl: 8192
osl: 1024
concurrencies: "64"
req_rate: inf
random_range_ratio: 0.8
use_chat_template: true
custom_tokenizer: "sa_bench_tokenizers.sglang_deepseek_v4.SGLangDeepseekV4Tokenizer"
frontend:
type: dynamo
enable_multiple_frontends: false
health_check:
max_attempts: 540
interval_seconds: 10
environment:
TRTLLM_SERVER_DISABLE_GC: '1'
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