NVIDIA RTX 3080 10GB
The NVIDIA RTX 3080 10GB has 10 GB VRAM and 760 GB/s memory bandwidth. It can run 33 of our 107 tracked models natively in VRAM at 8k context.
With 10 GB GDDR6X, the NVIDIA RTX 3080 10GB is a consumer-tier GPU that can run 33 models natively.
The NVIDIA RTX 3080 10GB was the mainstream Ampere GPU with 10GB GDDR6X at 760 GB/s. The 10GB capacity is tight for LLM inference: 7B models fit at Q4–Q5, but anything larger requires CPU offload. High memory bandwidth partially compensates, delivering decent tokens-per-second on models that do fit.
NVIDIA RTX 3080 10GB: 10GB GDDR6X at 760 GB/s, bandwidth excellent, capacity limiting.
7B at Q4-Q5. 8B tight. ~8-14 t/s for 7B.
Full CUDA support. 10GB is awkward for LLM; consider 12GB or 16GB instead.
| Vendor | NVIDIA |
| Architecture | Ampere |
| VRAM | 10 GB |
| Memory type | GDDR6X |
| Memory bandwidth | 760 GB/s |
| Compute backend | CUDA |
| Tier | Consumer |
| Released | 2020 |
| Models (native) | 33 / 107 |
| Models (offload) | 29 / 107 |
Software: Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.
Cloud GPU Rental
Don't want to buy an NVIDIA RTX 3080 10GB? RunPod is a cloud GPU rental service: rent one by the hour instead, no contract, no upfront hardware cost.
Pay by the hour · no contract · pods start in about a minute.
Rent an RTX 3080 10GB from $0.17/hr on RunPod ↗ (+$5 signup credit)Prices as of Oct 17, 2026. Rates change; check RunPod for current pricing.
Affiliate link: CanItRun may earn a commission. Doesn't affect the fit calculation above.
Popular models for this GPU
Models this GPU runs natively in VRAM (33)
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~64.9 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~74.3 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q3_K_M · ~66.4 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7Q2_K · ~73.2 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q3_K_M · ~66.8 t/s
Show 28 more
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6Q3_K_M · ~75.9 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6Q3_K_M · ~77.7 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2Q4_K_M · ~68.9 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0Q4_K_M · ~66.2 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5Q6_K · ~74.8 t/s
- Bonsai 2 9B9B · MMLU-Pro N/ATernary (Q2_0) · ~162.6 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/AQ6_K · ~74.8 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3Q6_K · ~74.9 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0Q6_K · ~74.9 t/s
- Qwen3 8B8B · MMLU-Pro 56.7Q6_K · ~72.5 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3Q6_K · ~85.4 t/s
- Gemma 4 E4B7.52B · MMLU-Pro 69.4Q8_0 · ~101 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0Q6_K · ~80.5 t/s
- Gemma 4 E2B4.65B · MMLU-Pro 60.0Q8_0 · ~171.2 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6Q8_0 · ~112.2 t/s
- Bonsai 2 4B4B · MMLU-Pro N/ATernary (Q2_0) · ~242.1 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4Q8_0 · ~78.3 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3Q8_0 · ~104.5 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~79 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~84.2 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~94 t/s
- Bonsai 2 2B2B · MMLU-Pro N/ATernary (Q2_0) · ~414.4 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~112 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~153.4 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~197.3 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~198.4 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~324.1 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~285.9 t/s
Models that fit with CPU offload (29)
These use system RAM for layers that don't fit in VRAM, so expect much slower inference.
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q2_K · ~1.2 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q2_K · ~1.2 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q2_K · ~1.2 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q2_K · ~1.2 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q4_K_M · ~5.3 t/s
- Flint 40B (MoE)39.8B · MMLU-Pro N/AQ5_K_M · ~51.7 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q3_K_M · ~1.4 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q6_K · ~23.6 t/s
- Qwen 3.6 35B-A3B (MoE)35B · MMLU-Pro 85.2Q6_K · ~23.6 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ6_K · ~23.6 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q6_K · ~1.2 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q6_K · ~1.3 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q6_K · ~1.3 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q6_K · ~1.3 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q6_K · ~1.3 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q5_K_M · ~24.2 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q6_K · ~1.4 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q6_K · ~23.2 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q5_K_M · ~21.8 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ8_0 · ~1.2 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q6_K · ~1.5 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q8_0 · ~1.2 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q8_0 · ~1.2 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q8_0 · ~1.2 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ8_0 · ~1.2 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q8_0 · ~14.8 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q8_0 · ~1.4 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q8_0 · ~1.5 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~16.2 t/s
Too large for this GPU (45)
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Scout 109B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GPT-OSS 120B
- GLM-4.5 355B
- GLM-4.5 Air 106B
- GLM-4.6 355B
- GLM-4.6V 106B
- GLM-4.7 358B
- Qwen 3.5 122B-A10B (MoE)
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Nemotron 3 Super 120B
- Nemotron 3.5 Super VL 120B-A12B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- Mistral Medium 3.5 128B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- Step 3.5 Flash
- Step 3.7 Flash
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
- DeepSeek V4 Flash 0731 284B
- Qwen3.8 2.4T-A95B
- Qwen3.8-Flash-Next
- DeepSeek V4 Pro 0813 1.6T
- Muse Spark Open 816B-A42B
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- GLM-5.3-Flash 320B
- DeepSeek V4.1 Flash 552B
- DeepSeek V4.1 Flash 1017 552B
- Ornith 1.5 122B (MoE)
Compare NVIDIA RTX 3080 10GB with other GPUs
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Frequently asked questions
- How much VRAM does the NVIDIA RTX 3080 10GB have?
- The NVIDIA RTX 3080 10GB has 10 GB of GDDR6X with 760 GB/s memory bandwidth.
- What is the NVIDIA RTX 3080 10GB best for?
- With 10 GB of VRAM, the NVIDIA RTX 3080 10GB is best for running compact models (1B–8B) at low quantization, suitable for edge inference, prototyping, and lightweight tasks.
- What LLMs can the NVIDIA RTX 3080 10GB run locally?
- The NVIDIA RTX 3080 10GB can run 33 of the 107 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Ornith 1.5 9B at Q6_K, Qwen 3.5 9B at Q6_K, Bonsai 27B at Ternary (Q2_0).
- Can the NVIDIA RTX 3080 10GB run Gemma 4 31B?
- The NVIDIA RTX 3080 10GB can run Gemma 4 31B with CPU offload at Q6_K quantization, but inference will be slower than native VRAM execution.
- Can the NVIDIA RTX 3080 10GB run Qwen 3.6 27B?
- The NVIDIA RTX 3080 10GB can run Qwen 3.6 27B with CPU offload at Q8_0 quantization, but inference will be slower than native VRAM execution.
- Can the NVIDIA RTX 3080 10GB run Qwen3 8B?
- Yes. The NVIDIA RTX 3080 10GB runs Qwen3 8B natively in VRAM at Q6_K quantization, achieving approximately 72.5 tokens per second.
- Can I rent the NVIDIA RTX 3080 10GB instead of buying it?
- Yes: RunPod and similar cloud GPU providers let you rent NVIDIA RTX 3080 10GB instances by the hour, with no long-term contract. Rentals start from $0.17/hr on RunPod (as of Oct 17, 2026). This is often cheaper than buying if you only need it occasionally, and lets you try the GPU before committing to a purchase.