RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
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SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 4080 Super / chat

What can NVIDIA GeForce RTX 4080 Super run for chat?

Build: NVIDIA GeForce RTX 4080 Super + — + 32 GB RAM (windows)

Memory: 16 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
52 models

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

#1Granite 3.0 2B Instruct
2B
granite
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 7.7 GBHeadroom: 8.3 GBTTFT: instant
396
tok/s
E
Weights
1.21 GB
KV cache
0.50 GB
Activations
4.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~98 ms (instant)
Model details →Run-on benchmark page →
#2Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
ollama run mistral:7b
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#3Falcon 3 7B Instruct
7B
falcon
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#4InternLM 2.5 7B Chat
7B
internlm
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#5Qwen 3 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#6Llama 3.1 8B Instruct
8B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
ollama run llama3.1:8b
99
tok/s
E
Weights
4.83 GB
KV cache
0.27 GB
Activations
2.29 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →Run-on benchmark page →
#7Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast
ollama run qwen3:8b
99
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →Run-on benchmark page →
#8Llama 3.3 8B Instruct
8B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast
99
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →Run-on benchmark page →
#9Tulu 3 8B
8B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast
99
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →Run-on benchmark page →
#10Ministral 8B Instruct
8B
mistral
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast
99
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →Run-on benchmark page →
#11Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 10.7 GBHeadroom: 5.3 GBTTFT: fast
ollama run gemma2:9b
88
tok/s
E
Weights
5.43 GB
KV cache
1.13 GB
Activations
2.32 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~441 ms (fast)
Model details →Run-on benchmark page →
#12Gemma 3 1B
1B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GBTTFT: instant
ollama run gemma3:1b
792
tok/s
E
Weights
0.60 GB
KV cache
0.50 GB
Activations
8.22 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~49 ms (instant)
Model details →Run-on benchmark page →

Runs with tradeoffs
73 models

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

Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.8 GBHeadroom: 1.2 GBTTFT: fast
  • • Tight VRAM fit — only 1.2 GB headroom left for context growth
ollama run llama3.2:3b
150
tok/s
E
Weights
3.19 GB
KV cache
1.50 GB
Activations
8.35 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~147 ms (fast)
Model details →Run-on benchmark page →
Dolphin 3.0 Llama 3.2 3B
3B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 2.6 GBTTFT: fast
  • • Tight VRAM fit — only 2.6 GB headroom left for context growth
264
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~147 ms (fast)
Model details →Run-on benchmark page →
Hermes 3 Llama 3.2 3B
3B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 2.6 GBTTFT: fast
  • • Tight VRAM fit — only 2.6 GB headroom left for context growth
264
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~147 ms (fast)
Model details →Run-on benchmark page →
Granite 3.0 8B Instruct
8B
granite
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 13.0 GBHeadroom: 3.0 GBTTFT: fast
  • • Tight VRAM fit — only 3.0 GB headroom left for context growth
99
tok/s
E
Weights
4.83 GB
KV cache
2.00 GB
Activations
4.34 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →Run-on benchmark page →
Mistral Nemo 12B Instruct
12B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 13.0 GBHeadroom: 3.0 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.0 GB headroom left for context growth
ollama run mistral-nemo:12b
66
tok/s
E
Weights
7.25 GB
KV cache
1.50 GB
Activations
2.41 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~589 ms (noticeable)
Model details →Run-on benchmark page →
Gemma 3 12B
12B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 13.0 GBHeadroom: 3.0 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.0 GB headroom left for context growth
ollama run gemma3:12b
66
tok/s
E
Weights
7.25 GB
KV cache
1.50 GB
Activations
2.41 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~589 ms (noticeable)
Model details →Run-on benchmark page →
Stable LM 2 12B
12B
other
Quant: Q4_K_MContext: 2,048VRAM: 13.0 GBHeadroom: 3.0 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.0 GB headroom left for context growth
66
tok/s
E
Weights
7.25 GB
KV cache
1.50 GB
Activations
2.41 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~589 ms (noticeable)
Model details →Run-on benchmark page →
Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 14.5 GBHeadroom: 1.5 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3:14b
57
tok/s
E
Weights
8.45 GB
KV cache
1.75 GB
Activations
2.47 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~687 ms (noticeable)
Model details →Run-on benchmark page →

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: 1 new comfortable, 82 new tradeoff

  • • SmolLM 2 360M Instruct
  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Llama 3.3 70B Instruct
Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 3090 Ti

~$1199

24 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~736 GB/s to ~? GB/s.

Unlocks: 40 new comfortable

  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
  • • Phi-3.5 Vision
  • • Phi-3.5 Mini Instruct
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 4080 Super

~$1099

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: 57 new comfortable

  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
  • • Phi-3.5 Vision
  • • Phi-3.5 Mini Instruct
Shop this upgrade↗

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

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 (16 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 (16 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 (16 GB) + 60% of system RAM (19 GB) combined.

—
DeepSeek V4 Flash (284B MoE)
284B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 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 (16 GB) + 60% of system RAM (19 GB) combined.

—

How to read these numbers

M
Measured — we ran this exact combo on owner hardware.

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

E
Estimated — pure formula based on VRAM bandwidth and model architecture.

Full methodology →

Want a specific benchmark we don't have? Email support@runlocalai.co and we'll prioritize it.