What can NVIDIA GeForce RTX 4090 run for long context?

Build: RTX 4090 + Ryzen 9 7950X + 64GB DDR5

Memory: 24 GB VRAM + 64 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
143 models

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

#1Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 15.6 GBTTFT: fast
ollama run gemma4:e4b
154
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): ~124 ms (fast)
Model details →
#2Phi-3.5 Mini Instruct
3.8B
phi
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.0 GBHeadroom: 16.0 GBTTFT: fast
ollama run phi3.5:3.8b
162
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): ~118 ms (fast)
Model details →
#3Command R7B (12-2024)
8B
command-r
Quant: Q4_K_MContext: 8,192VRAM: 10.4 GBHeadroom: 13.6 GBTTFT: fast
136
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): ~248 ms (fast)
Model details →
#4Falcon Mamba 7B
7B
falcon
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GBTTFT: fast
155
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): ~217 ms (fast)
Model details →
#5Codestral Mamba 7B
7B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GBTTFT: fast
155
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): ~217 ms (fast)
Model details →
#6Ministral 3B Instruct
3B
mistral
Quant: Q4_K_MContext: 8,192VRAM: 5.3 GBHeadroom: 18.7 GBTTFT: instant
362
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): ~93 ms (instant)
Model details →
#7Ministral 8B Instruct
8B
mistral
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 12.9 GBTTFT: fast
136
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): ~248 ms (fast)
Model details →
#8Qwen 2.5 7B Instruct
7B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 13.2 GBTTFT: fast
ollama run qwen2.5:7b
88
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): ~217 ms (fast)
Model details →
#9Mistral Nemo 12B Instruct
12B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 16.9 GBHeadroom: 7.1 GBTTFT: fast
ollama run mistral-nemo:12b
79
tok/s
Estimated
Weights
8.70 GB
KV cache
6.00 GB
Activations
0.44 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~372 ms (fast)
Model details →
#10Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GBTTFT: fast
ollama run qwen3:14b
78
tok/s
Estimated
Weights
8.40 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~434 ms (fast)
Model details →
#11Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 9.6 GBTTFT: fast
ollama run qwen3:8b
77
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): ~248 ms (fast)
Model details →
#12Qwen 2.5 14B Instruct
14B
qwen
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 19.8 GBHeadroom: 4.2 GBTTFT: fast
ollama run qwen2.5:14b
68
tok/s
Estimated
Weights
10.50 GB
KV cache
7.00 GB
Activations
0.53 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~434 ms (fast)
Model details →

Runs with tradeoffs
73 models

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

Gemma 4 Turkish 26B (4B active)
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 20.1 GBHeadroom: 3.9 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.9 GB headroom left for context growth
42
tok/s
Estimated
Weights
14.30 GB
KV cache
3.25 GB
Activations
0.72 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~806 ms (noticeable)
Model details →
Gemma 3 27B
27B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run gemma3:27b
40
tok/s
Estimated
Weights
16.00 GB
KV cache
3.38 GB
Activations
0.80 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~837 ms (noticeable)
Model details →
Muse Glimmer 30B
30B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 20.4 GBHeadroom: 3.6 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run muse-glimmer
36
tok/s
Estimated
Weights
17.00 GB
KV cache
0.71 GB
Activations
0.86 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~930 ms (noticeable)
Model details →
Jamba 1.5 Mini
52B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 59.3 GBHeadroom: 3.1 GBTTFT: fast
  • • Partial CPU offload: ~60% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
8
tok/s
Estimated
Weights
30.00 GB
KV cache
26.00 GB
Activations
1.51 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~372 ms (fast)
Model details →
Nemotron 3 Nano (30B-A3B)
30B
other
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 50.4 GBHeadroom: 12.0 GBTTFT: noticeable
  • • Partial CPU offload: ~52% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run nemotron3:nano
2
tok/s
Estimated
Weights
32.00 GB
KV cache
15.00 GB
Activations
1.61 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~930 ms (noticeable)
Model details →
Llama 3.2 11B Vision Instruct
11B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 20.4 GBHeadroom: 3.6 GBTTFT: fast
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run llama3.2-vision:11b
56
tok/s
Estimated
Weights
12.50 GB
KV cache
5.50 GB
Activations
0.63 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~341 ms (fast)
Model details →
Command R 35B
35B
command-r
Quant: Q4_K_MContext: 8,192VRAM: 41.4 GBHeadroom: 21.0 GBTTFT: noticeable
  • • Partial CPU offload: ~42% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run command-r:35b
4
tok/s
Estimated
Weights
21.00 GB
KV cache
17.50 GB
Activations
1.06 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1085 ms (noticeable)
Model details →
Gemma 4 26B MoE
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.1 GB headroom left for context growth
ollama run gemma4:26b-moe
42
tok/s
Estimated
Weights
16.00 GB
KV cache
3.25 GB
Activations
0.80 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~806 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: 41 new comfortable, 79 new tradeoff

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

Upgrade to NVIDIA RTX PRO 4500 Blackwell

see current pricing

32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~1008 GB/s to ~896 GB/s.

Unlocks: 80 new comfortable

  • • Qwen 3 0.6B
  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Qwen3.6 27B
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 4090

~$1899

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: 93 new comfortable

  • • Qwen 3 0.6B
  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Qwen3.6 27B
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 (24 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 (24 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 (24 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 (24 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 (24 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.

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