[Docs] Clean up speculators docs (#34065)

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>

docs/features/speculative_decoding/README.md

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+# Speculative Decoding
+
+This document shows how to use [Speculative Decoding](https://arxiv.org/pdf/2302.01318) with vLLM to reduce inter-token latency under medium-to-low QPS (query per second), memory-bound workloads.
+
+To train your own draft models for optimized speculative decoding, see [vllm-project/speculators](speculators.md) for seamless training and integration with vLLM.
+
+## vLLM Speculation Methods
+
+vLLM supports a variety of methods of speculative decoding. Model-based methods such as EAGLE, draft models, and mlp provide the best latency reduction, while simpler methods such as n-gram and and suffix decoding provide modest speedups without increasing workload during peak traffic.
+
+- [EAGLE](eagle.md)
+- [Draft Model](draft_model.md)
+- [Multi-Layer Perceptron](mlp.md)
+- [N-Gram](n_gram.md)
+- [Suffix Decoding](suffix.md)
+
+## Lossless guarantees of Speculative Decoding
+
+In vLLM, speculative decoding aims to enhance inference efficiency while maintaining accuracy. This section addresses the lossless guarantees of
+speculative decoding, breaking down the guarantees into three key areas:
+
+1. **Theoretical Losslessness**
+   \- Speculative decoding sampling is theoretically lossless up to the precision limits of hardware numerics. Floating-point errors might
+   cause slight variations in output distributions, as discussed
+   in [Accelerating Large Language Model Decoding with Speculative Sampling](https://arxiv.org/pdf/2302.01318)
+
+2. **Algorithmic Losslessness**
+   \- vLLM’s implementation of speculative decoding is algorithmically validated to be lossless. Key validation tests include:
+
+    > - **Rejection Sampler Convergence**: Ensures that samples from vLLM’s rejection sampler align with the target
+    >   distribution. [View Test Code](https://github.com/vllm-project/vllm/blob/47b65a550866c7ffbd076ecb74106714838ce7da/tests/samplers/test_rejection_sampler.py#L252)
+    > - **Greedy Sampling Equality**: Confirms that greedy sampling with speculative decoding matches greedy sampling
+    >   without it. This verifies that vLLM's speculative decoding framework, when integrated with the vLLM forward pass and the vLLM rejection sampler,
+    >   provides a lossless guarantee. Almost all of the tests in [tests/spec_decode/e2e](/tests/v1/spec_decode).
+    >   verify this property using [this assertion implementation](https://github.com/vllm-project/vllm/blob/b67ae00cdbbe1a58ffc8ff170f0c8d79044a684a/tests/spec_decode/e2e/conftest.py#L291)
+
+3. **vLLM Logprob Stability**
+   \- vLLM does not currently guarantee stable token log probabilities (logprobs). This can result in different outputs for the
+   same request across runs. For more details, see the FAQ section
+   titled *Can the output of a prompt vary across runs in vLLM?* in the [FAQs](../../usage/faq.md).
+
+While vLLM strives to ensure losslessness in speculative decoding, variations in generated outputs with and without speculative decoding
+can occur due to following factors:
+
+- **Floating-Point Precision**: Differences in hardware numerical precision may lead to slight discrepancies in the output distribution.
+- **Batch Size and Numerical Stability**: Changes in batch size may cause variations in logprobs and output probabilities, potentially
+  due to non-deterministic behavior in batched operations or numerical instability.
+
+For mitigation strategies, please refer to the FAQ entry *Can the output of a prompt vary across runs in vLLM?* in the [FAQs](../../usage/faq.md).
+
+## Known Feature Incompatibility
+
+1. Pipeline parallelism is not composible with speculative decoding as of `vllm<=0.15.0`
+2. Speculative decoding with a draft models is not supported in `vllm<=0.10.0`
+
+## Resources for vLLM contributors
+
+- [[vLLM Office Hours #40] Intro to Speculators](https://www.youtube.com/watch?v=2ISAr_JVGLs)
+- [A Hacker's Guide to Speculative Decoding in vLLM](https://www.youtube.com/watch?v=9wNAgpX6z_4)
+- [What is Lookahead Scheduling in vLLM?](https://docs.google.com/document/d/1Z9TvqzzBPnh5WHcRwjvK2UEeFeq5zMZb5mFE8jR0HCs/edit#heading=h.1fjfb0donq5a)
+- [Information on batch expansion](https://docs.google.com/document/d/1T-JaS2T1NRfdP51qzqpyakoCXxSXTtORppiwaj5asxA/edit#heading=h.kk7dq05lc6q8)
+- [Dynamic speculative decoding](https://github.com/vllm-project/vllm/issues/4565)