What can NVIDIA GeForce RTX 5090 run?

Build: RTX 5090 + Ryzen 9 9950X + 64GB DDR5

Memory: 32 GB VRAM + 64 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
216 models

Full-VRAM resident, with room for context. No compromises.

#1Qwen 3 0.6B
0.6B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 29.6 GBTTFT: instant
3215
tok/s
Estimated
Weights
0.30 GB
KV cache
0.30 GB
Activations
0.02 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~12 ms (instant)
Model details →
#2Llama 3.1 8B Instruct
8B
llama
Commercial OK
Quant: FP16Context: 8,192VRAM: 19.8 GBHeadroom: 12.2 GBTTFT: fast
ollama run llama3.1:8b
73
tok/s
Estimated
Weights
16.10 GB
KV cache
1.07 GB
Activations
0.81 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~164 ms (fast)
Model details →
#3Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 24.5 GBHeadroom: 7.5 GBTTFT: noticeable
ollama run qwen3:30b
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 →
#4Qwen 2.5 Coder 32B Instruct
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 23.9 GBHeadroom: 8.1 GBTTFT: noticeable
ollama run qwen2.5-coder:32b
60
tok/s
Estimated
Weights
19.00 GB
KV cache
2.15 GB
Activations
0.96 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~655 ms (noticeable)
Model details →
#5Qwen3.6 27B
27B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 23.0 GBHeadroom: 9.0 GBTTFT: noticeable
ollama run qwen3.6:27b
71
tok/s
Estimated
Weights
17.00 GB
KV cache
3.38 GB
Activations
0.85 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~553 ms (noticeable)
Model details →
#6Qwen 3 32B
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 6.2 GBTTFT: noticeable
ollama run qwen3:32b
60
tok/s
Estimated
Weights
19.00 GB
KV cache
4.00 GB
Activations
0.95 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~655 ms (noticeable)
Model details →
#7Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 24.6 GBHeadroom: 7.4 GBTTFT: noticeable
ollama run gemma4:31b
62
tok/s
Estimated
Weights
18.00 GB
KV cache
3.88 GB
Activations
0.90 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~635 ms (noticeable)
Model details →
#8Qwen 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 →
#9Gemma 4 12B
12B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 15.8 GBHeadroom: 16.2 GBTTFT: fast
ollama run gemma4:12b
161
tok/s
Estimated
Weights
7.60 GB
KV cache
6.00 GB
Activations
0.39 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~246 ms (fast)
Model details →
#10Qwen3.5 9B
9B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.2 GBHeadroom: 18.8 GBTTFT: fast
ollama run qwen3.5:9b
214
tok/s
Estimated
Weights
6.60 GB
KV cache
4.50 GB
Activations
0.34 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~184 ms (fast)
Model details →
#11Qwen3 Coder 30B-A3B
30B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 25.5 GBHeadroom: 6.5 GBTTFT: noticeable
ollama run qwen3-coder:30b
64
tok/s
Estimated
Weights
19.00 GB
KV cache
3.75 GB
Activations
0.95 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~614 ms (noticeable)
Model details →
#12DeepSeek R1 Distill Qwen 32B
32B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 6.2 GBTTFT: noticeable
ollama run deepseek-r1:32b
60
tok/s
Estimated
Weights
19.00 GB
KV cache
4.00 GB
Activations
0.95 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~655 ms (noticeable)
Model details →

Runs with tradeoffs
43 models

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

Llama 3.3 70B Instruct
70B
llama
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 53.8 GBHeadroom: 16.6 GBTTFT: noticeable
  • • Partial CPU offload: ~41% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run llama3.3:70b
2
tok/s
Estimated
Weights
47.00 GB
KV cache
2.68 GB
Activations
2.36 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1434 ms (noticeable)
Model details →
DeepSeek R1 Distill Llama 70B
70B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 52.6 GBHeadroom: 17.8 GBTTFT: noticeable
  • • Partial CPU offload: ~39% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run deepseek-r1:70b
2
tok/s
Estimated
Weights
40.00 GB
KV cache
8.75 GB
Activations
2.00 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1434 ms (noticeable)
Model details →
Qwen3.6 35B-A3B
35B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 31.4 GBHeadroom: 0.6 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.6 GB headroom left for context growth
ollama run qwen3.6:35b-a3b
55
tok/s
Estimated
Weights
24.00 GB
KV cache
4.38 GB
Activations
1.20 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)
Model details →
Gemma 4 26B MoE
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 31.6 GBHeadroom: 0.4 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run gemma4:26b-moe
74
tok/s
Estimated
Weights
16.00 GB
KV cache
13.00 GB
Activations
0.81 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~532 ms (noticeable)
Model details →
Gemma 4 26B-A4B
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 31.6 GBHeadroom: 0.4 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run gemma4:26b-a4b-it-q4_K_M
74
tok/s
Estimated
Weights
16.00 GB
KV cache
13.00 GB
Activations
0.81 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~532 ms (noticeable)
Model details →
Mistral Small 3 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
ollama run mistral-small:24b
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 →
Qwen3.5 35B-A3B
35B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 31.4 GBHeadroom: 0.6 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.6 GB headroom left for context growth
ollama run qwen3.5:35b-a3b
55
tok/s
Estimated
Weights
24.00 GB
KV cache
4.38 GB
Activations
1.20 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)
Model details →
Llama 3.1 70B Instruct
70B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 52.6 GBHeadroom: 17.8 GBTTFT: noticeable
  • • Partial CPU offload: ~39% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run llama3.1:70b
2
tok/s
Estimated
Weights
40.00 GB
KV cache
8.75 GB
Activations
2.00 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1434 ms (noticeable)
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: 48 new tradeoff

  • • Llama 4 Scout
  • • Llama 3.3 70B Instruct
  • • DeepSeek R1 Distill Llama 70B
  • • Qwen3.6 35B-A3B
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: 21 new comfortable

  • • Qwen3.6 35B-A3B
  • • Gemma 4 26B MoE
  • • Gemma 4 26B-A4B
  • • Mistral Small 3 24B
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: 39 new comfortable

  • • Llama 3.3 70B Instruct
  • • DeepSeek R1 Distill Llama 70B
  • • Qwen3.6 35B-A3B
  • • Gemma 4 26B MoE
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 (38 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 (38 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 (38 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 (38 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 (38 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.

RunLocalAI Will-It-Run Framework →

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