Advisor / Hardware / RTX 5090 32GB

Best local LLMs for RTX 5090 32GB

GeForce RTX 5090. Memory-fit math with KV cache, speeds labelled by how they were obtained, and commands you can paste.

31.0 GBusable for model + KV cache
1792 GB/smemory bandwidth
24 GBRAM available for offload
38measured data points on file
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Best for Coding · 16k context

ModelQuantFitMemorySpeed (generation)
Gemma 4 31BQ6_Kfits fully27.4 GB~55 tok/s measured src
Qwen3.6 27BQ8_0fits fully29.8 GB~49 tok/s scaled from measured src
Qwen3-Coder 30B-A3BQ6_Kfits fully26.7 GB~316 tok/s scaled from measured src
Qwen3.6 35B-A3BUD-Q6_Kfits fully29.7 GB~260 tok/s measured src
Gemma 4 26B-A4BQ8_0fits fully27.9 GB~213 tok/s scaled from measured src
llama.cpp · top pick
llama-server -hf unsloth/gemma-4-31B-it-GGUF:Q6_K -c 16384 -fa on --jinja -ngl 99
ollama · top pick
OLLAMA_CONTEXT_LENGTH=16384 ollama run hf.co/unsloth/gemma-4-31B-it-GGUF:Q6_K

Best for Chat / general · 16k context

ModelQuantFitMemorySpeed (generation)
Gemma 4 31BQ6_Kfits fully27.4 GB~55 tok/s measured src
Qwen3.6 27BQ8_0fits fully29.8 GB~49 tok/s scaled from measured src
Mistral Small 3.2 24BQ8_0fits fully27.7 GB~56 tok/s scaled from measured src
Gemma 4 12BQ8_0fits fully13.7 GB~91 tok/s scaled from measured src
Qwen3.6 35B-A3BUD-Q6_Kfits fully29.7 GB~260 tok/s measured src
llama.cpp · top pick
llama-server -hf unsloth/gemma-4-31B-it-GGUF:Q6_K -c 16384 -fa on --jinja -ngl 99
ollama · top pick
OLLAMA_CONTEXT_LENGTH=16384 ollama run hf.co/unsloth/gemma-4-31B-it-GGUF:Q6_K

Best for Vision · 16k context

ModelQuantFitMemorySpeed (generation)
Gemma 4 31BQ6_Kfits fully27.4 GB~55 tok/s measured src
Qwen3.6 27BQ8_0fits fully29.8 GB~49 tok/s scaled from measured src
Mistral Small 3.2 24BQ8_0fits fully27.7 GB~56 tok/s scaled from measured src
Gemma 4 12BQ8_0fits fully13.7 GB~91 tok/s scaled from measured src
Qwen3.6 35B-A3BUD-Q6_Kfits fully29.7 GB~260 tok/s measured src
llama.cpp · top pick
llama-server -hf unsloth/gemma-4-31B-it-GGUF:Q6_K -c 16384 -fa on --jinja -ngl 99
ollama · top pick
OLLAMA_CONTEXT_LENGTH=16384 ollama run hf.co/unsloth/gemma-4-31B-it-GGUF:Q6_K

All 11 models that fit, fastest first · 16k context

Click a column header to sort. 11 of 11 speeds here are measured or scaled from measured; the rest are bandwidth estimates. Filter by use case, speed source, fit, license and more in the interactive advisor →

ModelParamsQuantFitMemoryTok/sSource
Qwen3-Coder 30B-A3B30.53B MoEQ6_Kfits fully26.7 GB~316scaled from measured
gpt-oss 20B20.91B MoEQ8_0fits fully12.5 GB~298scaled from measured
Qwen3.6 35B-A3B35.95B MoEUD-Q6_Kfits fully29.7 GB~260measured
Gemma 4 26B-A4B25.81B MoEQ8_0fits fully27.9 GB~213scaled from measured
Qwen3.5 9B9.65BQ8_0fits fully10.1 GB~110scaled from measured
Gemma 4 12B11.96BQ8_0fits fully13.7 GB~91scaled from measured
Qwen3.5 4B4.66BQ8_0fits fully5.1 GB~72scaled from measured
Mistral Small 3.2 24B24.01BQ8_0fits fully27.7 GB~56scaled from measured
Gemma 4 31B31.27BQ6_Kfits fully27.4 GB~55measured
Qwen3.6 27B27.78BQ8_0fits fully29.8 GB~49scaled from measured
Qwen3-VL 8B8.77BQ8_0fits fully11.1 GB~43scaled from measured

Long context on this hardware · up to 1M tokens

Largest context whose KV cache still fits next to the weights (same placement as the 16k result, KV types f16 / q8_0 / q4_0). Models are capped at their verified maximum; extended means beyond the native maximum using YaRN, only listed where the model card documents it. Long context needs flash attention (-fa on, already in the commands) and a lot of memory; a quantized KV cache saves memory but can cost accuracy. Speed at depth is an estimate (penalty fitted to measured depth runs up to 128k).

Try it in the advisor: 128k · 256k · 512k · 1M

ModelNative maxMax ctx f16Max ctx q8_0Max ctx q4_0Est. tok/s at max f16 ctx
Qwen3.5 9B Q8_0256k512k extended1M extended1M extended~48 estimated
Qwen3.5 4B Q8_0256k512k extended1M extended1M extended~19 estimated
Gemma 4 12B Q8_0256k256k256k256k~73 estimated
gpt-oss 20B Q8_0128k128k128k128k~141 estimated
Gemma 4 26B-A4B Q8_0256k128k256k256k~140 estimated
Qwen3-VL 8B Q8_0256k128k256k512k extended~17 estimated
Qwen3.6 35B-A3B UD-Q6_K256k64k128k256k~186 estimated
Qwen3-Coder 30B-A3B Q6_K256k32k64k128k~170 estimated
Mistral Small 3.2 24B Q8_0128k32k64k64k~48 estimated
Gemma 4 31B Q6_K256k32k64k128k~50 estimated
Qwen3.6 27B Q8_0256k32k32k64k~46 estimated
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Open the interactive advisor with this hardware →

Speeds marked measured are medians of real runs (short-context generation) from these sources; scaled from measured means a measurement of a same-size or same-model-different-quant run, scaled by bytes read; estimated comes from memory bandwidth (spec source: https://www.techpowerup.com/gpu-specs/geforce-rtx-5090.c4216) × an efficiency factor of 0.68 calibrated to measured runs on this hardware. 38 measured data points on file for this hardware · submit your benchmark. System RAM here is an example (32 GB, ddr5-5600). Model data last verified 2026-10-14. Ranking = fits and fast enough first, then larger; not a quality benchmark.

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