Runtime and community-driven improvements such as KV cache management, expert caching, GGUF quantization, and speculative decoding are bringing large open LLMs to consumer and legacy hardware. This is making 100k+ token contexts and multi-GPU on-premise setups more practical.
A benchmark of 21 quantized Qwen3.8 27B variants on an RTX 5080 with 16GB VRAM shows how much GGUF choice matters. The bartowski IQ4_XS variant offers the lowest mean KL divergence and highest same-top-p agreement, while more aggressive quants prove ...
A builder spent €18,000 on an EPYC server with 768 GB of VRAM and 256 GB of RAM to run frontier open source LLMs locally. But upcoming models such as GLM 6 could be two to three times larger, making the machine unusable at 4-bit quantization. The cas...
A user built a multiplayer FPS with a local Qwen 3.8 Flash Next model, Q4_K_XL quantization and 256k context, using opencode. Playable demo in two hours, three days of refinement, 20 tokens/s avg with MTP on RTX 5090 + RTX 4000 PRO. It highlights the...
The Qwen3.8-Flash-Next case on dual RTX 3090s shows that for MoE models VRAM is no longer the only variable: memory hierarchy and expert caching shift decode from 17 to 29 tokens/s. A concrete signal for teams evaluating on-premise deployments on old...
A Reddit post reports a drop from 15 hours of active programming to 4 hours for debugging or implementing a feature with Qwen 27B, even at 0.5 tokens per second. The data reframes expectations for asynchronous inference and the value of recovered hum...
For local LLMs, parameter count is not the whole constraint: the KV cache grows with every token and can become the real bottleneck at 100k or 200k context. GQA, MQA, and quantization reduce its footprint, but growth remains linear. Future optimizati...
A user documents a jump from 17 to 25-29 tokens/s on two RTX 3090s with a full 261,888-token context, using llama.cpp's LRU expert cache and a smaller ubatch. The setup keeps 48 expert layers in system RAM and shows meaningful headroom for on-prem in...
Perplexity has open-sourced the lily repository inside pplx-garden, a Mac inference server built around a single model, Qwen 3.6, to get the best performance on Apple Silicon. This is not general-purpose serving: vertical integration around one model...
The Reddit post is thin, but the message is clear: MTP support for Qwen3.8-Flash-Next in GGUF format promises to raise tokens per second in llama.cpp runs. More than a single optimization, it signals the direction of the open-source stack for local i...
Support for RadixArk/Qwen3.8-Flash-Next-NVFP4 in SGLang-V100 shows four V100 32GB running full 256k-token context with over 50GB offloaded to system RAM. Prefill around 4,000 tokens/s and decoding around 60 tokens/s. MTP data signal throughput gains ...
A three-and-a-half-hour test on a 128GB MacBook Pro M5 Max ran a 79GB 2-bit Qwen3.8-Flash-Next with a 358,400-token context slot. Prefill fell from 1,561 to 318 tokens/s; generation from 30–35 to 11.5 tokens/s at 169K. After 100K tokens the model mix...
A documented Reddit setup runs a Qwen3.8-27B on an RTX 4070 Ti SUPER at 47-50 tokens/s with a 100,000-token context window. The credit goes to the beellama.cpp runtime and the asymmetric kvarn5/kvarn4 KV cache, which freed about 6% VRAM. A precision ...
