What can NVIDIA GeForce RTX 5080 run for chat?

Build: RTX 5080 + Ryzen 9 9950X + 32GB DDR5

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

Runs comfortably
83 models

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

#1Qwen 3 0.6B
0.6B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 13.6 GBTTFT: instant
1723
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): ~27 ms (instant)
Model details →
#2Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: fast
ollama run qwen3:8b
129
tok/s
Estimated
Weights
4.80 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~366 ms (fast)
Model details →
#3Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 9.1 GBTTFT: fast
ollama run llama3.2:3b
196
tok/s
Estimated
Weights
3.40 GB
KV cache
1.50 GB
Activations
0.18 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~137 ms (fast)
Model details →
#4Qwen 3 1.7B
1.7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 12.4 GBTTFT: instant
608
tok/s
Estimated
Weights
0.90 GB
KV cache
0.85 GB
Activations
0.05 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~78 ms (instant)
Model details →
#5TinyLlama 1.1B Chat v1.0
1.1B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 13.4 GBTTFT: instant
940
tok/s
Estimated
Weights
0.60 GB
KV cache
0.14 GB
Activations
0.03 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~50 ms (instant)
Model details →
#6Gemma 2 2B Instruct
2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GBTTFT: instant
517
tok/s
Estimated
Weights
1.10 GB
KV cache
1.00 GB
Activations
0.06 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~91 ms (instant)
Model details →
#7Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 5.3 GBTTFT: fast
ollama run mistral:7b
130
tok/s
Estimated
Weights
5.10 GB
KV cache
3.50 GB
Activations
0.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~320 ms (fast)
Model details →
#8Trendyol LLM 7B Chat v0.1
7B
llama
Quant: Q4_K_MContext: 4,096VRAM: 7.6 GBHeadroom: 8.4 GBTTFT: fast
148
tok/s
Estimated
Weights
3.90 GB
KV cache
1.75 GB
Activations
0.20 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~320 ms (fast)
Model details →
#9Kumru 2B
2.4B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 11.4 GBTTFT: fast
ollama run alibayram/kumru:latest
431
tok/s
Estimated
Weights
1.50 GB
KV cache
1.20 GB
Activations
0.08 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~110 ms (fast)
Model details →
#10Turkcell LLM 7B v1
7.4B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.2 GBHeadroom: 5.8 GBTTFT: fast
ollama run RefinedNeuro/Turkcell-LLM-7b-v1:latest
140
tok/s
Estimated
Weights
4.50 GB
KV cache
3.70 GB
Activations
0.23 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~338 ms (fast)
Model details →
#11Mistral Turkish v2 (brooqs)
7.2B
mistral
Quant: Q4_0Context: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBTTFT: fast
ollama run brooqs/mistral-turkish-v2:latest
154
tok/s
Estimated
Weights
4.10 GB
KV cache
3.60 GB
Activations
0.21 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~329 ms (fast)
Model details →
#12Malhajar Mistral 7B Turkish
7.2B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: fast
ollama run koezgen/malhajar-mistral-tur7b:latest
126
tok/s
Estimated
Weights
5.10 GB
KV cache
3.60 GB
Activations
0.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~329 ms (fast)
Model details →

Runs with tradeoffs
46 models

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

DeepSeek V2 Lite Chat
15.7B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 12.8 GBHeadroom: 3.2 GBTTFT: fast
  • • Tight VRAM fit — only 3.2 GB headroom left for context growth
431
tok/s
Estimated
Weights
8.60 GB
KV cache
1.96 GB
Activations
0.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~110 ms (fast)
Model details →
Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: fast
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run gemma2:9b
115
tok/s
Estimated
Weights
5.80 GB
KV cache
4.50 GB
Activations
0.30 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~411 ms (fast)
Model details →
Mistral Nemo 12B Instruct
12B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 15.7 GBHeadroom: 0.3 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.3 GB headroom left for context growth
ollama run mistral-nemo:12b
86
tok/s
Estimated
Weights
7.50 GB
KV cache
6.00 GB
Activations
0.38 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~549 ms (noticeable)
Model details →
Gemma 3 12B
12B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 0.5 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.5 GB headroom left for context growth
ollama run gemma3:12b
86
tok/s
Estimated
Weights
7.30 GB
KV cache
6.00 GB
Activations
0.37 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~549 ms (noticeable)
Model details →
Stable LM 2 12B
12B
other
Quant: Q4_K_MContext: 4,096VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
86
tok/s
Estimated
Weights
7.20 GB
KV cache
3.00 GB
Activations
0.36 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~549 ms (noticeable)
Model details →
Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run qwen3:14b
74
tok/s
Estimated
Weights
8.40 GB
KV cache
1.75 GB
Activations
0.42 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~640 ms (noticeable)
Model details →
DeepSeek MoE 16B Base
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 15.8 GBHeadroom: 0.2 GBTTFT: fast
  • • Tight VRAM fit — only 0.2 GB headroom left for context growth
431
tok/s
Estimated
Weights
9.50 GB
KV cache
4.00 GB
Activations
0.48 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~110 ms (fast)
Model details →
GLM-4 9B
9B
glm
Quant: Q4_K_MContext: 8,192VRAM: 12.1 GBHeadroom: 3.9 GBTTFT: fast
  • • Tight VRAM fit — only 3.9 GB headroom left for context growth
115
tok/s
Estimated
Weights
5.50 GB
KV cache
4.50 GB
Activations
0.28 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~411 ms (fast)
Model details →

What if you upgraded?

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

+32 GB system RAM

Check the current price

Adds 32 GB to your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 5 new comfortable, 57 new tradeoff

  • • Gemma 3 270M
  • • SmolLM2 135M Instruct
  • • SmolLM2 360M Instruct
  • • VBART Large (Turkish Summarization)
Shop this upgrade↗

Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)

Check the current price

22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~960 GB/s to ~616 GB/s.

Unlocks: 24 new comfortable

  • • Qwen 3 14B
  • • Gemma 3 270M
  • • Phi-4 14B
  • • Phi-4 Reasoning 14B

Add a second NVIDIA GeForce RTX 5080

Launch MSRP $999 (2025). Check the current price.

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: 45 new comfortable

  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Qwen 3 32B
  • • Gemma 4 31B Dense
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 (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 R1 (671B reasoning)
671B
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.

—
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.

—
Cloud GPU rental

Not enough VRAM for these models? Rent a GPU by the hour

The models above need more memory than 16 GB VRAM + 32 GB system RAM. You can rent a GPU with enough VRAM by the hour to run one of them, or to test it before you buy hardware. For daily use, owning a card usually costs less over time.

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Rent on Vast.ai

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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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