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FPGAs for speculative decoding

BACKGROUND
2 SIGNALSFIRST DETECTED 3 May 2026
Narrative synthesis pending — 2 signals collected.
How the story developed
3 May
FPGAs for speculative decoding
OPINION · WHAT · LocalLLaMA
5.9
Other feeds and industry voices
26 Apr
Speculative Decoding Implementations: EAGLE-3, Medusa-1, PARD, Draft Models, N-gram and Suffix Decoding from scratch

<!-- SC_OFF --><div class="md"><p>I’ve been working on an educational implementation repo for speculative decoding:</p> <p><a

OPINION · WHAT · LocalLLaMAOPEN STORY
10 Apr
How are you handling KV cache memory pressure in production? HBM keeps filling up

<!-- SC_OFF --><div class="md"><p>Running Llama-3-70B with long contexts and hitting HBM limits constantly. 128K context burns approx 40GB just for KV cache — half the H100 gone

OPINION · HOW · LocalLLaMAOPEN STORY
26 Mar
1 million tokens per second from a single cluster, what that actually means

<!-- SC_OFF --><div class="md"><p>Got Qwen 3.5 27B to 1,103,941 tok/s on 12 nodes with 96 B200 GPUs.</p> <p>At that rate you process 50,000 insurance policy documents in hours

OPINION · WHAT · singularityOPEN STORY
5 May
MTP on strix halo with llama.cpp (PR #22673)

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t4uj9h/mtp_on_strix_halo_with_llamacpp_pr_22673/"> <img alt="MTP on strix halo with llama.cpp (PR #22673)"

OPINION · WHAT · LocalLLaMAOPEN STORY
8 Apr
Napkin math: KV cache for Llama 3.1 at 128K context is ~64GB per decode request, and that's after GQA

<!-- SC_OFF --><div class="md"><p>Every output token the model generates requires reading the entire KV cache from High Bandwidth Memory.</p> <p>Here's what that actually looks

OPINION · WHAT · LocalLLaMAOPEN STORY
13 Apr
FYI, Step 3.5 Flash has better perf and context is 1/4 the price in llama.cpp

<!-- SC_OFF --><div class="md"><p>So i recently updated LMstudio after a long pause and updated my llama.cpp runtimes too.. i was shocked.. i thought maybe something like

OPINION · WHAT · LocalLLaMAOPEN STORY
1 May
Running llama.cpp on Snapdragon Hexagon NPU seems promising

<!-- SC_OFF --><div class="md"><p><a

OPINION · WHAT · LocalLLaMAOPEN STORY
24 Mar
FlashAttention-4: 1613 TFLOPs/s, 2.7x faster than Triton, written in Python. What it means for inference.

<!-- SC_OFF --><div class="md"><p>Wrote a deep dive on <strong>FlashAttention-4 (03/05/2026)</strong> that's relevant for anyone thinking about inference performance.</p>

OPINION · WHAT · LocalLLaMAOPEN STORY
Sources
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