[None][doc] Add DeepSeek-V3.2-Exp document #9141
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Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
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📝 WalkthroughWalkthroughDocumentation for DeepSeek models expanded to include DeepSeek-V3.2-Exp alongside V3 and R1. Updates cover hardware requirements, usage guidance with explicit tokens_per_block parameters, FlashMLA and KV Cache behavior differences, and a new Known Issues section detailing KV Cache Reuse and Chunked Prefill limitations. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~15 minutes
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examples/models/core/deepseek_v3/README.md (5)
4-4: Simplify redundant phrasing."Share exact same" is wordy; consider replacing with "share the same."
-**DeepSeek-R1 and DeepSeek-V3 share exact same model architecture other than weights differences, and share same code path in TensorRT-LLM. DeepSeek-V3.2-Exp features DeepSeek Sparse Attention (DSA), but otherwise shares the same code as DeepSeek-R1 and DeepSeek-V3 in TensorRT-LLM. For brevity we only provide one model example, the example command to be used interchangeably by only replacing the model name to the other one**. +**DeepSeek-R1 and DeepSeek-V3 share the same model architecture other than weights differences, and share the same code path in TensorRT-LLM. DeepSeek-V3.2-Exp features DeepSeek Sparse Attention (DSA), but otherwise shares the same code as DeepSeek-R1 and DeepSeek-V3 in TensorRT-LLM. For brevity we only provide one model example, the example command to be used interchangeably by only replacing the model name to the other one**.
18-18: Fix markdown link fragment to match heading format.The table of contents link uses the typographic en-dash character (
‑), which may not resolve correctly in the markdown anchor. Ensure the link fragment matches the heading's slug format (lowercase, regular hyphens).- - [DeepSeek‑V3, DeepSeek-R1, and DeepSeek-V3.2-Exp](#deepseekv3-deepseek-r1-and-deepseekv32-exp) + - [DeepSeek‑V3, DeepSeek-R1, and DeepSeek-V3.2-Exp](#deepseek-v3-deepseek-r1-and-deepseek-v32-exp)
750-750: Correct subject-verb agreement for compound subject."FP8 KV Cache and MLA" is a compound subject and should take the plural verb "are" rather than "is."
-FP8 KV Cache and MLA is supported on Hopper and Blackwell for DeepSeek-V3 and DeepSeek-R1, while it is only supported on Blackwell for DeepSeek-V3.2-Exp. The accuracy loss is small, with GSM8k accuracy drop less than 1%. +FP8 KV Cache and MLA are supported on Hopper and Blackwell for DeepSeek-V3 and DeepSeek-R1, while they are only supported on Blackwell for DeepSeek-V3.2-Exp. The accuracy loss is small, with GSM8k accuracy drop less than 1%.
869-874: Specify language for fenced code block.The YAML configuration block should include a language identifier for proper syntax highlighting.
-- DeepSeek-V3.2-Exp cannot support KV Cache Reuse and Chunked Prefill now. When running `quickstart_advanced.py`, please include `--disable_kv_cache_reuse` to disable KV Cache Reuse. When using `trtllm-eval`/`trtllm-serve`/`trtllm-bench`, please include the following configuration in the extra llm_api options: -``` +- DeepSeek-V3.2-Exp cannot support KV Cache Reuse and Chunked Prefill now. When running `quickstart_advanced.py`, please include `--disable_kv_cache_reuse` to disable KV Cache Reuse. When using `trtllm-eval`/`trtllm-serve`/`trtllm-bench`, please include the following configuration in the extra llm_api options: +```yaml kv_cache_config: enable_block_reuse: false tokens_per_block: 64 enable_chunked_prefill: false -``` +```
1-1: Ensure consistent model name formatting throughout the document.The title (line 1) uses a typographic en-dash for "DeepSeek‑V3," but most other references in the document use a regular hyphen "DeepSeek-V3" (e.g., lines 92, 110, 750). Choose one consistent style and apply it throughout for professional appearance.
Consider standardizing to either:
- Typographic en-dash (
‑) for all references, or- Regular hyphen (
-) for all referencesThe regular hyphen is typically easier to maintain in source and more compatible across tools.
Also applies to: 92-92, 110-110, 742-742, 750-750, 867-874
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🧠 Learnings (17)
📓 Common learnings
Learnt from: timlee0212
Repo: NVIDIA/TensorRT-LLM PR: 6886
File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0
Timestamp: 2025-08-14T06:36:40.701Z
Learning: In DeepSeek V3 model (tensorrt_llm/_torch/models/modeling_deepseekv3.py), the disagreement between AllReduce.__init__ guard and _compute_mlp_tp_size logic for MNNVL usage is expected by design. The AllReduce component and MLP TP-size computation intentionally use different criteria for MNNVL availability decisions.
📚 Learning: 2025-08-14T06:36:40.701Z
Learnt from: timlee0212
Repo: NVIDIA/TensorRT-LLM PR: 6886
File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0
Timestamp: 2025-08-14T06:36:40.701Z
Learning: In DeepSeek V3 model (tensorrt_llm/_torch/models/modeling_deepseekv3.py), the disagreement between AllReduce.__init__ guard and _compute_mlp_tp_size logic for MNNVL usage is expected by design. The AllReduce component and MLP TP-size computation intentionally use different criteria for MNNVL availability decisions.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-08-21T00:16:56.457Z
Learnt from: farshadghodsian
Repo: NVIDIA/TensorRT-LLM PR: 7101
File: docs/source/blogs/tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.md:36-36
Timestamp: 2025-08-21T00:16:56.457Z
Learning: TensorRT-LLM container release tags in documentation should only reference published NGC container images. The README badge version may be ahead of the actual published container versions.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-08-01T15:14:45.673Z
Learnt from: yibinl-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 6506
File: examples/models/core/mixtral/requirements.txt:3-3
Timestamp: 2025-08-01T15:14:45.673Z
Learning: In TensorRT-LLM, examples directory can have different dependency versions than the root requirements.txt file. Version conflicts between root and examples dependencies are acceptable because examples are designed to be standalone and self-contained.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-08-11T20:09:24.389Z
Learnt from: achartier
Repo: NVIDIA/TensorRT-LLM PR: 6763
File: tests/integration/defs/triton_server/conftest.py:16-22
Timestamp: 2025-08-11T20:09:24.389Z
Learning: In the TensorRT-LLM test infrastructure, the team prefers simple, direct solutions (like hard-coding directory traversal counts) over more complex but robust approaches when dealing with stable directory structures. They accept the maintenance cost of updating tests if the layout changes.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-09-09T09:40:45.658Z
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-08-18T08:42:02.640Z
Learnt from: samuellees
Repo: NVIDIA/TensorRT-LLM PR: 6974
File: tensorrt_llm/serve/scripts/benchmark_dataset.py:558-566
Timestamp: 2025-08-18T08:42:02.640Z
Learning: In TensorRT-LLM's RandomDataset (tensorrt_llm/serve/scripts/benchmark_dataset.py), when using --random-token-ids option, sequence length accuracy is prioritized over semantic correctness for benchmarking purposes. The encode/decode operations should use skip_special_tokens=True and add_special_tokens=False to ensure exact target token lengths.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-08-15T06:46:53.813Z
Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6767
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-15T06:46:53.813Z
Learning: In the TensorRT-LLM KV cache manager, SWA (Sliding Window Attention) combined with beam search is currently in a broken/non-functional state and is planned for future rework. During preparatory refactoring phases, code related to SWA+beam search may intentionally remain in a non-working state until the broader rework is completed.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-08-20T07:43:36.447Z
Learnt from: ChristinaZ
Repo: NVIDIA/TensorRT-LLM PR: 7068
File: cpp/tensorrt_llm/kernels/moeTopKFuncs.cuh:169-172
Timestamp: 2025-08-20T07:43:36.447Z
Learning: In TensorRT-LLM MOE kernels, when processing up to 128 experts across 32 threads, each thread handles at most 4 experts (N < 5 constraint), where N represents candidates per thread rather than total system capacity.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-08-21T02:39:12.009Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 7104
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1475-1480
Timestamp: 2025-08-21T02:39:12.009Z
Learning: The min latency mode functionality in TensorRT-LLM MOE kernels (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu) is deprecated and no longer being maintained/updated, as confirmed by djns99. Bug reports and optimization suggestions for the computeStridesTmaWarpSpecializedLowLatencyKernel and related min latency code paths should be deprioritized.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-09-23T15:13:48.819Z
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/kernels/nccl_device/multimem.h:20-30
Timestamp: 2025-09-23T15:13:48.819Z
Learning: TRT-LLM targets modern CUDA toolkits that support FP8 datatypes, so cuda_fp8.h can be included unconditionally without version guards in TRT-LLM code.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-09-23T15:12:38.312Z
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device implementation, NCCL version 2.28+ requirements are handled at runtime in the nccl_device/config layer rather than with compile-time guards. This allows the allreduceOp to remain version-agnostic and delegates version compatibility validation to the appropriate lower-level components that can gracefully handle unsupported configurations.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-09-19T21:28:13.751Z
Learnt from: jhaotingc
Repo: NVIDIA/TensorRT-LLM PR: 7856
File: cpp/tensorrt_llm/thop/fp8BlockScaleMoe.cpp:159-166
Timestamp: 2025-09-19T21:28:13.751Z
Learning: In TensorRT-LLM blockScaleMoe routing (cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/runner.cu), the DeepSeek routing method performs reinterpret_cast<float*>(routingLogits) at line 89, which could cause issues if routing_logits are BF16. However, Qwen3-FP8 models use RenormalizeNaive routing method and are not affected by this dtype casting issue.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-08-21T21:48:35.135Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 7104
File: cpp/tensorrt_llm/cutlass_extensions/include/cutlass_extensions/epilogue/fusion/sm90_visitor_scatter.hpp:399-417
Timestamp: 2025-08-21T21:48:35.135Z
Learning: CUTLASS extensions in TensorRT-LLM (located under cpp/tensorrt_llm/cutlass_extensions/) are designed to integrate with and extend functionality in the external CUTLASS repository. When analyzing these extensions, their consumers and functionality wiring may exist in the CUTLASS codebase rather than within TensorRT-LLM itself.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-07-17T09:01:27.402Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Applied to files:
examples/models/core/deepseek_v3/README.md
📚 Learning: 2025-08-26T09:49:04.956Z
Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 7192
File: tests/integration/test_lists/test-db/l0_dgx_b200.yml:56-72
Timestamp: 2025-08-26T09:49:04.956Z
Learning: In TensorRT-LLM test configuration files, the test scheduling system handles wildcard matching with special rules that prevent duplicate test execution even when the same tests appear in multiple yaml files with overlapping GPU wildcards (e.g., "*b200*" and "*gb200*").
Applied to files:
examples/models/core/deepseek_v3/README.md
🪛 LanguageTool
examples/models/core/deepseek_v3/README.md
[style] ~4-~4: ‘exact same’ might be wordy. Consider a shorter alternative.
Context: ...nd. **DeepSeek-R1 and DeepSeek-V3 share exact same model architecture other than weights d...
(EN_WORDINESS_PREMIUM_EXACT_SAME)
🪛 markdownlint-cli2 (0.18.1)
examples/models/core/deepseek_v3/README.md
18-18: Link fragments should be valid
(MD051, link-fragments)
869-869: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
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