What can NVIDIA GeForce RTX 5090 run for long context?

Build: NVIDIA GeForce RTX 5090 + — + 32 GB RAM (windows)

Memory: 32 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
175 models

Ranked by fit for long context use case + predicted speed. Click a row for VRAM breakdown.

#1Nemotron 3 Nano (30B-A3B)
30B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 24.5 GBHeadroom: 7.5 GBTTFT: noticeable
ollama run nemotron3:nano
64
tok/s
Estimated
Weights
18.00 GB
KV cache
3.75 GB
Activations
0.90 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~614 ms (noticeable)
Model details →
#2Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 17.6 GBTTFT: fast
ollama run qwen3:8b
137
tok/s
Estimated
Weights
8.20 GB
KV cache
4.00 GB
Activations
0.42 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~164 ms (fast)
Model details →
#3Qwen 2.5 7B Instruct
7B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 21.2 GBTTFT: fast
ollama run qwen2.5:7b
157
tok/s
Estimated
Weights
8.10 GB
KV cache
0.47 GB
Activations
0.41 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~143 ms (fast)
Model details →
#4Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 23.6 GBTTFT: instant
ollama run gemma4:e4b
274
tok/s
Estimated
Weights
4.40 GB
KV cache
2.00 GB
Activations
0.23 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~82 ms (instant)
Model details →
#5Phi-3.5 Mini Instruct
3.8B
phi
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.0 GBHeadroom: 24.0 GBTTFT: instant
ollama run phi3.5:3.8b
288
tok/s
Estimated
Weights
4.10 GB
KV cache
1.90 GB
Activations
0.21 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~78 ms (instant)
Model details →
#6Command R7B (12-2024)
8B
command-r
Quant: Q4_K_MContext: 8,192VRAM: 10.4 GBHeadroom: 21.6 GBTTFT: fast
241
tok/s
Estimated
Weights
4.40 GB
KV cache
4.00 GB
Activations
0.23 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~164 ms (fast)
Model details →
#7Falcon Mamba 7B
7B
falcon
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 22.3 GBTTFT: fast
276
tok/s
Estimated
Weights
4.20 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~143 ms (fast)
Model details →
#8Codestral Mamba 7B
7B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 22.3 GBTTFT: fast
276
tok/s
Estimated
Weights
4.20 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~143 ms (fast)
Model details →
#9Ministral 3B Instruct
3B
mistral
Quant: Q4_K_MContext: 8,192VRAM: 5.3 GBHeadroom: 26.7 GBTTFT: instant
643
tok/s
Estimated
Weights
1.90 GB
KV cache
1.50 GB
Activations
0.10 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~61 ms (instant)
Model details →
#10Ministral 8B Instruct
8B
mistral
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 20.9 GBTTFT: fast
241
tok/s
Estimated
Weights
5.00 GB
KV cache
4.00 GB
Activations
0.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~164 ms (fast)
Model details →
#11Mistral Nemo 12B Instruct
12B
mistral
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 21.5 GBHeadroom: 10.5 GBTTFT: fast
ollama run mistral-nemo:12b
91
tok/s
Estimated
Weights
13.00 GB
KV cache
6.00 GB
Activations
0.66 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~246 ms (fast)
Model details →
#12Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 24.6 GBHeadroom: 7.4 GBTTFT: fast
ollama run qwen3:14b
78
tok/s
Estimated
Weights
15.00 GB
KV cache
7.00 GB
Activations
0.76 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~287 ms (fast)
Model details →

Runs with tradeoffs
26 models

Tight VRAM, partial CPU offload, or context-limited.

Jamba 1.5 Mini
52B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 39.8 GBHeadroom: 11.4 GBTTFT: fast
  • • Partial CPU offload: ~20% of layers run on CPU
161
tok/s
Estimated
Weights
30.00 GB
KV cache
6.50 GB
Activations
1.50 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~246 ms (fast)
Model details →
Gemma 4 Turkish 26B (4B active)
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 29.8 GBHeadroom: 2.2 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.2 GB headroom left for context growth
74
tok/s
Estimated
Weights
14.30 GB
KV cache
13.00 GB
Activations
0.72 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~532 ms (noticeable)
Model details →
Command R 35B
35B
command-r
Quant: Q4_K_MContext: 2,048VRAM: 28.2 GBHeadroom: 3.8 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.8 GB headroom left for context growth
ollama run command-r:35b
55
tok/s
Estimated
Weights
21.00 GB
KV cache
4.38 GB
Activations
1.05 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)
Model details →
Nemotron 3 Nano Omni 33B
33B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 47.7 GBHeadroom: 3.5 GBTTFT: noticeable
  • • Partial CPU offload: ~33% of layers run on CPU
ollama run nemotron3:33b
58
tok/s
Estimated
Weights
28.00 GB
KV cache
16.50 GB
Activations
1.41 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~676 ms (noticeable)
Model details →
Llama 3.3 70B Instruct
70B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 46.5 GBHeadroom: 4.7 GBTTFT: noticeable
  • • Partial CPU offload: ~31% of layers run on CPU
ollama run llama3.3:70b
28
tok/s
Estimated
Weights
40.00 GB
KV cache
2.68 GB
Activations
2.01 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1434 ms (noticeable)
Model details →
Mistral Small 3.2 24B
24B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 3.5 GBTTFT: fast
  • • Tight VRAM fit — only 3.5 GB headroom left for context growth
80
tok/s
Estimated
Weights
14.00 GB
KV cache
12.00 GB
Activations
0.71 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~492 ms (fast)
Model details →
Mistral Medium 3 24B (dense)
24B
mistral
Quant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 3.5 GBTTFT: fast
  • • Tight VRAM fit — only 3.5 GB headroom left for context growth
80
tok/s
Estimated
Weights
14.00 GB
KV cache
12.00 GB
Activations
0.71 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~492 ms (fast)
Model details →
Devstral Small 2 24B
24B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 3.5 GBTTFT: fast
  • • Tight VRAM fit — only 3.5 GB headroom left for context growth
80
tok/s
Estimated
Weights
14.00 GB
KV cache
12.00 GB
Activations
0.71 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~492 ms (fast)
Model details →

What if you upgraded?

Hypothetical scenarios. We re-ran the compatibility engine for each.

+32 GB system RAM

~$80–150

Doubles your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 41 new comfortable, 43 new tradeoff

  • • Qwen 3 0.6B
  • • Qwen 3 1.7B
  • • Gemma 3 270M
  • • SmolLM2 135M Instruct
Shop this upgrade↗

Upgrade to NVIDIA A100 40GB

see current pricing

40 GB VRAM (vs your 32 GB) plus a bandwidth jump from ~1792 GB/s to ~1555 GB/s.

Unlocks: 62 new comfortable

  • • Qwen 3 0.6B
  • • Qwen3.6 35B-A3B
  • • Qwen 3 1.7B
  • • Gemma 4 26B MoE
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 5090

~$2499

Tensor parallelism splits the model across both cards, effectively doubling VRAM. Bandwidth doesn't double — runs ~1.5× the single-card speed in practice.

Unlocks: 80 new comfortable

  • • Qwen 3 0.6B
  • • Llama 3.3 70B Instruct
  • • DeepSeek R1 Distill Llama 70B
  • • Qwen3.6 35B-A3B
Shop this upgrade↗

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Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

DeepSeek V4 Pro (1.6T MoE)
1600B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (19 GB) combined.

—
Qwen 3.5 235B-A17B (MoE)
397B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (19 GB) combined.

—
Qwen 3 235B-A22B
235B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (19 GB) combined.

—
DeepSeek R1 (671B reasoning)
671B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (19 GB) combined.

—
Llama 4 Scout
109B
llama
Commercial OK

Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (19 GB) combined.

—

How to read these numbers

Measured here
Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed
Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

Extrapolated
Extrapolated - predicted from a measured benchmark on similar-bandwidth hardware.

Estimated
Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

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