Yes, just — 1.1 GB spare

LLM-jp 4 33B Thinking at Q4_K_M fits your GeForce RTX 3090 Ti entirely on the GPU at 8K context, at an estimated 33 tokens per second. Past 12K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.

Fully on GPU 8K context Q4_K_M · 18.7 GB Apache 2.0 Released 14 Aug 2026 New this week Not in the Ollama library

Japan’s national-institute reasoning model, Japanese and English. A plain dense Llama-style 33B: Q4 is a tight 24 GB fit.

What hardware do I need for LLM-jp 4 33B Thinking? →

The VRAM budget

weights 18.7 GB

Weights 18.7 GB KV cache @ 8K 2.00 GB Runtime overhead 0.6 GB Free 1.1 GB of 22.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	32.9 GB	35.5 GB	—	~2.6	−0.1% ppl	13.1 GB over
Q6_K	25.4 GB	28.0 GB	—	~5.4	−0.4% ppl	5.6 GB over
Q5_K_M	21.9 GB	24.5 GB	—	~11	−0.8% ppl	2.1 GB over
Q4_K_M	18.7 GB	21.3 GB	12K	33	−1.9% ppl	Recommended
Q3_K_M	15.1 GB	17.7 GB	26K	40	−5.4% ppl	Long context
Q2_K	12.9 GB	15.5 GB	35K	47	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.

How to run it

llama.cpp LM Studio vLLM

terminal

$ llama-server \
    -hf llm-jp/llm-jp-4-33b-thinking:Q4_K_M \
    -c 8192 -ngl 99

The engine underneath most of the others. Every knob is exposed. More on llama.cpp.

01Download is 18.7 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.

03Only 1.1 GB is spare, so a long system prompt can still push it over. Its real ceiling here is 12K context.

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