Running Qwen3.8-Next-Flash on 96GB RAM: offload the n-gram table to SSD

Iory1998 · reddit · 2026-08-30

A user with dual 5070 Ti + 3090 (40GB VRAM) and 96GB RAM shares a hands-on debugging story running Qwen3.8-Next-Flash Q4 locally:

Unsloth quants failed: Q4KXL (105GB) and IQ4XS (90GB) bake the 56B n-gram table into the weight files, so everything loads into memory; with KV cache, conversations beyond 60K–100K tokens crash llama.cpp silently, at only 12–14 t/s.
The AtomicChat build is the key difference: it shards the n-gram table onto SSD, needing only 54GB of fast memory. The table is touched sparsely (2.7KB per token via hash), so SSD offload costs almost nothing.
Results: a real 217K-token conversation ran end-to-end without crashing, full 256K context fits, and speed rose to 22 t/s. Prefill remains hardware-bound at 18 min for 220K tokens.
Fun fact: the AtomicChat option was suggested by DeepSeek running benchmarks on his own rig. The author cautions quant quality parity with unsloth isn't guaranteed.

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