What Models?

Pick the right model for your GPU — in seconds.

What AI models can an RTX 2070 SUPER (8 GB) run?

The GeForce RTX 2070 SUPER has 8 GB of VRAM and 448 GB/s memory bandwidth. 29 models run well on it and 5 more are a tight fit.

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Best for general use

Highest MMLU score among models that run well

  1. Qwen3.5 9B Q4_K_M 87K context · ~71 tok/s · MMLU 82.5
  2. Qwen3.5 4B Q8_0 123K context · ~87 tok/s · MMLU 79.1
  3. Qwen3 8B Q4_K_M 21K context · ~74 tok/s · MMLU 79

Best for coding

Highest SWE-bench Verified score among models that run well

  1. K2-Horizon 7B Q4_K_M 20K context · ~72 tok/s · SWE-bench 70.6
  2. Ornith 1.5 9B Q4_K_M 84K context · ~70 tok/s · SWE-bench 70.6
  3. MiMo V2.6 Distill Qwen 9B Q4_K_M 82K context · ~70 tok/s · SWE-bench 61.1

Upgrade options

The cheapest GPUs with more VRAM than the RTX 2070 SUPER, by launch MSRP.

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Every model on the RTX 2070 SUPER

52 of 200 model configurations can run on 8 GB — sorted by quality

100 models, each counted once by the best way it runs on your hardware.

Largest that runs well Nemotron Nano 12B 12.6B params · Q4_K_M · 27K context
Fastest good-quality model Phi-4 Mini 3.8B ~128 tok/s · Q4_K_M · 45K context
? MMLU (Massive Multitask Language Understanding) measures general knowledge across 57 subjects. Higher = more capable. Sorted best-first.

Runs well 29

  • Qwen3.5 9B 9B Great VisionTool useReasoning
    MMLU 82.5
    Q4_K_M
    Context 87K Speed ~71 tok/s
    Q8_0 Doesn't fit · needs 8.9 GB
  • Qwen3.5 4B 4B Great VisionTool useReasoning
    MMLU 79.1
    Q4_K_M
    Context 175K Speed ~126 tok/s
    Q8_0
    Context 123K Speed ~87 tok/s
  • Qwen3 8B 8.2B Great ReasoningTool use
    MMLU 79
    Q4_K_M
    Context 21K Speed ~74 tok/s
    Q8_0 Doesn't fit · needs 8.8 GB

Runs well 29

  • Qwen3.5 9B 9B Great VisionTool useReasoning
    MMLU 82.5
    Q4_K_M
    Context 87K Speed ~71 tok/s
    Q8_0 Doesn't fit · needs 8.9 GB
  • Qwen3.5 4B 4B Great VisionTool useReasoning
    MMLU 79.1
    Q4_K_M
    Context 175K Speed ~126 tok/s
    Q8_0
    Context 123K Speed ~87 tok/s
  • Qwen3 8B 8.2B Great ReasoningTool use
    MMLU 79
    Q4_K_M
    Context 21K Speed ~74 tok/s
    Q8_0 Doesn't fit · needs 8.8 GB
  • Gemma 4 12B 11.96B Great VisionTool useReasoning
    MMLU 77.2
    Q4_K_M
    Context 91K Speed ~59 tok/s
    Q8_0 Doesn't fit · needs 11.8 GB
  • Nemotron Nano 12B 12.6B Great ReasoningTool use
    MMLU 76
    Q4_K_M
    Context 27K Speed ~54 tok/s
    Q8_0 Doesn't fit · needs 13.1 GB
  • Qwen 2.5 7B 7.62B Good Tool use
    MMLU 74.2
    Q4_K_M
    Context 62K Speed ~79 tok/s
    Q8_0 Doesn't fit · needs 8.2 GB
  • Qwen 2.5 VL 7B 7.62B Good VisionTool use
    MMLU 74.2
    Q4_K_M
    Context 62K Speed ~79 tok/s
    Q8_0 Doesn't fit · needs 8.2 GB
  • Llama 3.2 Vision 11B 10.6B Good VisionTool use
    MMLU 73
    Q4_K_M
    Context 16K Speed ~64 tok/s
    Q8_0 Doesn't fit · needs 10.5 GB
  • Phi-4 Mini 3.8B 3.84B Good
    MMLU 70
    Q4_K_M
    Context 45K Speed ~128 tok/s
    Q8_0
    Context 32K Speed ~88 tok/s
  • Gemma 4 E4B 4.5B Good VisionTool useReasoning
    MMLU 69.4
    Q4_K_M
    Context 128K Speed ~74 tok/s
    Q8_0
    Context 34K Speed ~53 tok/s
  • Llama 3.1 8B 8.03B Good Tool use
    MMLU 69.4
    Q4_K_M
    Context 25K Speed ~76 tok/s
    Q8_0 Doesn't fit · needs 8.7 GB
    fp16 Doesn't fit · needs 16.2 GB
  • Qwen 2.5 Coder 7B 7.62B Good
    MMLU 68
    Q4_K_M
    Context 62K Speed ~79 tok/s
    Q8_0 Doesn't fit · needs 8.2 GB
  • Qwen 2.5 VL 3B 3.09B Fair Vision
    MMLU 65.6
    Q4_K_M
    Context 128K Speed ~153 tok/s
    Q8_0
    Context 128K Speed ~104 tok/s
  • Qwen 2.5 3B 3.09B Fair Tool use
    MMLU 65.6
    Q4_K_M
    Context 32K Speed ~153 tok/s
    Q8_0
    Context 32K Speed ~104 tok/s
  • SmallThinker 3B 3.09B Fair Reasoning
    MMLU 65
    Q4_K_M
    Context 32K Speed ~153 tok/s
    Q8_0
    Context 32K Speed ~104 tok/s
  • Ministral 8B 8.02B Fair Tool use
    MMLU 65
    Q4_K_M
    Context 21K Speed ~74 tok/s
    Q8_0 Doesn't fit · needs 8.7 GB
  • Llama 3.2 3B 3.21B Fair Tool use
    MMLU 63.4
    Q4_K_M
    Context 55K Speed ~148 tok/s
    Q8_0
    Context 42K Speed ~101 tok/s
    fp16 Tight fit · 14K context
  • Gemma 3 4B 3.88B Fair Vision
    MMLU 63
    Q4_K_M
    Context 128K Speed ~128 tok/s
    Q8_0
    Context 128K Speed ~87 tok/s
  • Gemma 4 E2B 2.3B Fair VisionTool useReasoning
    MMLU 60
    Q4_K_M
    Context 128K Speed ~106 tok/s
    Q8_0
    Context 128K Speed ~80 tok/s
  • DeepSeek R1 Distill Qwen 7B 7.62B Fair Reasoning
    MMLU 58
    Q4_K_M
    Context 62K Speed ~79 tok/s
    Q8_0 Doesn't fit · needs 8.2 GB
  • Qwen3.5 2B 2B Fair VisionTool useReasoning
    MMLU 55.3
    Q4_K_M
    Context 256K Speed ~205 tok/s
    Q8_0
    Context 256K Speed ~156 tok/s
  • Llama 3.2 1B 1.24B Basic Tool use
    MMLU 49.3
    Q4_K_M
    Context 128K Speed ~248 tok/s
    Q8_0
    Context 128K Speed ~193 tok/s
  • Qwen3.5 0.8B 0.8B Basic VisionTool useReasoning
    MMLU 29.7
    Q4_K_M
    Context 256K Speed ~299 tok/s
    Q8_0
    Context 256K Speed ~255 tok/s
  • Nemotron 3 Nano 4B 3.97B Basic ReasoningTool use
    MMLU
    Q4_K_M
    Context 256K Speed ~119 tok/s
    Q8_0
    Context 256K Speed ~91 tok/s
  • LFM2.5 2.6B 2.7B Basic Tool use
    MMLU
    Q4_K_M
    Context 128K Speed ~175 tok/s
    Q8_0
    Context 128K Speed ~122 tok/s
  • Ornith 1.5 9B 9.65B Basic Tool useReasoning
    MMLU
    Q4_K_M
    Context 84K Speed ~70 tok/s
    Q8_0 Doesn't fit · needs 9.1 GB
  • MiMo V2.6 Distill Qwen 9B 9.41B Basic VisionTool useReasoning
    MMLU
    Q4_K_M
    Context 82K Speed ~70 tok/s
    Q8_0 Doesn't fit · needs 8.9 GB
  • Nemotron 3.5 Bolt E4B 8B Basic Tool useReasoning
    MMLU
    Q4_K_M
    Context 64K Speed ~66 tok/s
    Q8_0
    Context 34K Speed ~53 tok/s
  • K2-Horizon 7B 9B Basic Tool useReasoning
    MMLU
    Q4_K_M
    Context 20K Speed ~72 tok/s
    Q8_0 Doesn't fit · needs 9.1 GB

Tight fit 5

  • Gemma 3 12B 11.8B Good Vision
    MMLU 74
    Q4_K_M
    Context 11K Speed ~54 tok/s

    Only 11K context — under the 16K needed to be practical

    Q8_0 Doesn't fit · needs 12.6 GB
    GPUs that can run this

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  • Gemma 2 9B 9.24B Good
    MMLU 71.3
    Q4_K_M
    Context 6K Speed ~66 tok/s

    Only 6K context — under the 16K needed to be practical

    Q8_0 Doesn't fit · needs 10.2 GB
  • Phi-3 Mini 3.8B 3.82B Good
    MMLU 70.9
    Q4_K_M
    Context 15K Speed ~132 tok/s

    Only 15K context — under the 16K needed to be practical

    Q8_0
    Context 10K Speed ~89 tok/s

    Only 10K context — under the 16K needed to be practical

    GPUs that can run this

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Tight fit 5

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Doesn't fit 66

  • DeepSeek R1 0528 671B Excellent Reasoning
    MMLU 90.8
    Q4_K_M Needs 409.3 GB — 401.3 GB over
    Q8_0 Needs 713.4 GB — 705.4 GB over
  • Qwen3 Next 80B-A3B 80B Excellent Tool use
    MMLU 90
    Q4_K_M Needs 48.4 GB — 40.4 GB over
    Q8_0 Needs 84.8 GB — 76.8 GB over
    GPUs that can run this

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  • MiMo V2.5 Pro 1020B Excellent Reasoning
    MMLU 89.4
    Q4_K_M Needs 630.1 GB — 622.1 GB over
    Q8_0 Needs 1090.1 GB — 1082.1 GB over

Doesn't fit 66