[None][doc] Add feature docs for helix parallelism (#9684)

Signed-off-by: Balaram Buddharaju <169953907+brb-nv@users.noreply.github.com>

Author: brb-nv

docs/source/features/helix.md

@@ -0,0 +1,82 @@
+# Helix Parallelism
+
+Helix is a context parallelism (CP) technique for the decode/generation phase of LLM inference. Unlike traditional attention-FFN disaggregation (AFD) techniques, which spatially separate attention and FFN blocks onto different GPUs, Helix temporally separates them by reconfiguring the same GPUs.
+
+For all details, see the original paper:
+[Helix Parallelism: Rethinking Sharding Strategies for
+Interactive Multi-Million-Token LLM Decoding](https://arxiv.org/pdf/2507.07120)
+
+## How Helix Works
+
+In Helix parallelism:
+
+- **KV cache distribution**: The KV cache is partitioned across CP ranks during generation, with each rank responsible for a portion of the cached context
+- **Attention computation**: Each rank computes partial attention over its local KV cache shard
+- **Attention postprocessing**: Partial results are combined / corrected across ranks to produce the final attention output
+- **FFN layers**: CP ranks are repurposed as tensor parallelism (TP) ranks for FFN/MoE layers, maximizing GPU utilization
+
+## When to Use Helix
+
+Helix parallelism provides performance benefits when **all** of the following conditions apply:
+
+1. **Disaggregated serving**: Helix is designed for generation servers in a disaggregated (prefill/decode split) deployment architecture
+2. **Long input sequences**: Performance gains typically appear with input sequence lengths **>64K tokens** or more
+3. **Low batch sizes**: Optimal for latency-sensitive workloads with high tokens/second/user requirements
+
+On a typical latency vs. throughput Pareto curve, Helix targets operating points toward the right side (low latency, high per-user throughput).
+
+## Supported Models
+
+Helix parallelism currently supports models using **Multi-head Latent Attention (MLA)** on Blackwell GPU architecture:
+
+- DeepSeek-V3 / DeepSeek-V3-Lite
+
+## Configuration
+
+### Configuration Parameters
+
+Please set the following parameters for the generation servers in disaggregated mode. Example can be seen in the e2e accuracy test mentioned below. 
+
+| Parameter | Description | Required |
+|-----------|-------------|----------|
+| `context_parallel_size` | Number of GPUs for context parallelism (≥2 for Helix) | Yes |
+| `cp_config.cp_type` | Must be `"HELIX"` or `CpType.HELIX` | Yes |
+| `cp_config.tokens_per_block` | Tokens per KV cache block | Yes |
+| `kv_cache_config.tokens_per_block` | Must match `cp_config.tokens_per_block` | Yes |
+
+### JSON Configuration (for YAML/JSON configs)
+
+```json
+{
+    "context_parallel_size": 2,
+    "cp_config": {
+        "cp_type": "HELIX",
+        "tokens_per_block": 32
+    },
+    "kv_cache_config": {
+        "tokens_per_block": 32
+    }
+}
+```
+
+## Testing Helix with TensorRT-LLM
+
+### Unit Test: MLA Module Correctness
+
+The simplest correctness test validates the [MLA attention module](../../../tensorrt_llm/_torch/modules/attention.py) with Helix enabled:
+
+```bash
+# Run the MLA Helix unit test
+pytest tests/unittest/_torch/modules/test_mla_helix.py -v
+```
+
+This test verifies that attention outputs match between single-GPU and Helix-parallelized execution.
+
+### End-to-End Accuracy test
+
+For end-to-end validation, the accuracy benchmark evaluates DeepSeek-V3-Lite in disaggregated mode on MMLU and GSM8K benchmarks:
+
+Test location: `tests/integration/defs/accuracy/test_disaggregated_serving.py`  
+Test name: `TestDeepSeekV3Lite::test_auto_dtype_with_helix`
+
+This test demonstrates proper disaggregated server configuration with Helix.

docs/source/index.rst

@@ -76,6 +76,7 @@ Welcome to TensorRT LLM's Documentation!
    features/auto_deploy/auto-deploy.md
    features/ray-orchestrator.md
    features/torch_compile_and_piecewise_cuda_graph.md
+   features/helix.md
 
 
 .. toctree::