What can NVIDIA GeForce RTX 3090 run for chat?

Build: NVIDIA GeForce RTX 3090 + — + 32 GB RAM (windows)

Memory: 24 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
108 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: 21.6 GB
1705
tok/s
Estimated
Weights
0.30 GB
KV cache
0.30 GB
Activations
0.02 GB
Runtime
1.80 GB
Model details →
#2Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 17.1 GB
ollama run llama3.2:3b
194
tok/s
Estimated
Weights
3.40 GB
KV cache
1.50 GB
Activations
0.18 GB
Runtime
1.80 GB
Model details →
#3Qwen 3 1.7B
1.7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 20.4 GB
602
tok/s
Estimated
Weights
0.90 GB
KV cache
0.85 GB
Activations
0.05 GB
Runtime
1.80 GB
Model details →
#4TinyLlama 1.1B Chat v1.0
1.1B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 21.4 GB
930
tok/s
Estimated
Weights
0.60 GB
KV cache
0.14 GB
Activations
0.03 GB
Runtime
1.80 GB
Model details →
#5Gemma 2 2B Instruct
2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 20.0 GB
511
tok/s
Estimated
Weights
1.10 GB
KV cache
1.00 GB
Activations
0.06 GB
Runtime
1.80 GB
Model details →
#6Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 13.3 GB
ollama run mistral:7b
128
tok/s
Estimated
Weights
5.10 GB
KV cache
3.50 GB
Activations
0.26 GB
Runtime
1.80 GB
Model details →
#7DeepSeek V2 Lite Chat
15.7B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 18.7 GBHeadroom: 5.3 GB
426
tok/s
Estimated
Weights
8.60 GB
KV cache
7.85 GB
Activations
0.44 GB
Runtime
1.80 GB
Model details →
#8Trendyol LLM 7B Chat v0.1
7B
llama
Quant: Q4_K_MContext: 4,096VRAM: 7.6 GBHeadroom: 16.4 GB
146
tok/s
Estimated
Weights
3.90 GB
KV cache
1.75 GB
Activations
0.20 GB
Runtime
1.80 GB
Model details →
#9Kumru 2B
2.4B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 19.4 GB
ollama run alibayram/kumru:latest
426
tok/s
Estimated
Weights
1.50 GB
KV cache
1.20 GB
Activations
0.08 GB
Runtime
1.80 GB
Model details →
#10Turkcell LLM 7B v1
7.4B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.2 GBHeadroom: 13.8 GB
ollama run RefinedNeuro/Turkcell-LLM-7b-v1:latest
138
tok/s
Estimated
Weights
4.50 GB
KV cache
3.70 GB
Activations
0.23 GB
Runtime
1.80 GB
Model details →
#11Mistral Turkish v2 (brooqs)
7.2B
mistral
Quant: Q4_0Context: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB
ollama run brooqs/mistral-turkish-v2:latest
152
tok/s
Estimated
Weights
4.10 GB
KV cache
3.60 GB
Activations
0.21 GB
Runtime
1.80 GB
Model details →
#12Malhajar Mistral 7B Turkish
7.2B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 13.2 GB
ollama run koezgen/malhajar-mistral-tur7b:latest
125
tok/s
Estimated
Weights
5.10 GB
KV cache
3.60 GB
Activations
0.26 GB
Runtime
1.80 GB
Model details →

Runs with tradeoffs
23 models

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

Gemma 3 27B
27B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GB
  • • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run gemma3:27b
38
tok/s
Estimated
Weights
16.00 GB
KV cache
3.38 GB
Activations
0.80 GB
Runtime
1.80 GB
Model details →
Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 36.2 GBHeadroom: 7.0 GB
  • • Partial CPU offload: ~34% of layers run on CPU
ollama run gemma4:31b
33
tok/s
Estimated
Weights
18.00 GB
KV cache
15.50 GB
Activations
0.91 GB
Runtime
1.80 GB
Model details →
Yi 1.5 34B
34B
yi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 39.8 GBHeadroom: 3.4 GB
  • • Partial CPU offload: ~40% of layers run on CPU
ollama run yi:34b
30
tok/s
Estimated
Weights
20.00 GB
KV cache
17.00 GB
Activations
1.01 GB
Runtime
1.80 GB
Model details →
Mixtral 8x7B Instruct
47B
mixtral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 37.1 GBHeadroom: 6.1 GB
  • • Partial CPU offload: ~35% of layers run on CPU
ollama run mixtral:8x7b
22
tok/s
Estimated
Weights
28.00 GB
KV cache
5.88 GB
Activations
1.40 GB
Runtime
1.80 GB
Model details →
Jamba 1.5 Mini
52B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 39.8 GBHeadroom: 3.4 GB
  • • Partial CPU offload: ~40% of layers run on CPU
85
tok/s
Estimated
Weights
30.00 GB
KV cache
6.50 GB
Activations
1.50 GB
Runtime
1.80 GB
Model details →
Llama 3.2 11B Vision Instruct
11B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 20.4 GBHeadroom: 3.6 GB
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run llama3.2-vision:11b
53
tok/s
Estimated
Weights
12.50 GB
KV cache
5.50 GB
Activations
0.63 GB
Runtime
1.80 GB
Model details →
Gemma 4 Turkish 26B (4B active)
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 20.1 GBHeadroom: 3.9 GB
  • • Tight VRAM fit — only 3.9 GB headroom left for context growth
39
tok/s
Estimated
Weights
14.30 GB
KV cache
3.25 GB
Activations
0.72 GB
Runtime
1.80 GB
Model details →
InternVL 2.5 26B
26B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GB
  • • Tight VRAM fit — only 2.1 GB headroom left for context growth
39
tok/s
Estimated
Weights
16.00 GB
KV cache
3.25 GB
Activations
0.80 GB
Runtime
1.80 GB
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, 35 new tradeoff

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

Upgrade to NVIDIA GeForce RTX 5090

Launch MSRP $1,999 (2025). Check the current price.

32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~? GB/s to ~1792 GB/s.

Unlocks: 22 new comfortable

  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Qwen 3 32B
  • • Gemma 4 31B Dense
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 3090

Launch MSRP $1,499 (2020). Now sells well below its launch price on the used market. 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: 28 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 (24 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 (24 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 (24 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 (24 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 (24 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 24 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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The two tiers run on different hosts and terms. Check which tier a pod is on before a long job.

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Vast.ai

billed per GPU-hour

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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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