RTX 3080 10G — what LLMs can it run?
56 of 110 indexed models fit comfortably in 10GB at 4K context, each at the highest-quality quant that still leaves headroom.
The short answer for RTX 3080 10G
10 GB, 760 GB/s GDDR6X. 56 of 110 models in this index fit comfortably at 4K context; 56 load at all.
- Biggest that fits
- DeepSeek-Coder-V2-Lite Instruct — 8.3 GB at AWQ INT4 — 1.7 GB spare at 4K, so longer context comes out of a thin margin.
- Room to grow
- GLM-4-9B-Chat — 5.9 GB at Q4_K_M — under 60% of the card, which leaves 4.1 GB for a long context window or a second process.
- Speed ceiling
- Llama 3.1 8B Instruct at Q4_K_M reads 4.6 GB of weights per token, so 760 GB/s puts a hard ceiling near 165 tok/s. That is arithmetic on two published numbers, not a benchmark — real throughput lands below it. No run on this card has been measured here, so there is nothing to compare the ceiling against.
- The wall you will hit
- Capacity. 56 of 110 models clear 10 GB comfortably at 4K context, and the ones that do not are short by whole gigabytes rather than by a rounding error — at 760 GB/s the memory system is not what holds this card back. Moving up a size class is what changes the list, not a different quant.
14B · 17
| Model | Quant | Est. VRAM | Headroom | tok/s |
|---|---|---|---|---|
| DeepSeek-Coder-V2-Lite Instruct16B-A2.4B | AWQ INT4 | 8.33 GB | +1.7 GB | ≤666Ceiling |
| DeepSeek-V2-Lite Chat16B-A2.4B | AWQ INT4 | 8.33 GB | +1.7 GB | ≤666Ceiling |
| StarCoder2 15B15B | GPTQ INT4 | 8.52 GB | +1.5 GB | ≤107Ceiling |
| Qwen3 14B Instruct14B | AWQ INT4 | 8.4 GB | +1.6 GB | ≤108Ceiling |
| Qwen2.5 14B Instruct14B | AWQ INT4 | 8.51 GB | +1.5 GB | ≤109Ceiling |
| DeepSeek-R1-Distill-Qwen-14B14B | AWQ INT4 | 8.51 GB | +1.5 GB | ≤109Ceiling |
| Phi-4 14B14B | AWQ INT4 | 8.54 GB | +1.5 GB | ≤109Ceiling |
| Phi-3 Medium 14B Instruct14B | AWQ INT4 | 8.17 GB | +1.8 GB | ≤114Ceiling |
| Mistral Nemo 12B Instruct12B | Q4_K_M | 8.42 GB | +1.6 GB | 71–95Derived |
| Gemma 3 12B IT12B | Q4_K_M | 8.52 GB | +1.5 GB | 71–95Derived |
| Mellum2.1 12B-A2.5B Thinking12B-A2.5B | Q4_K_M | 8.59 GB | +1.4 GB | ≤480Ceiling |
| Stable LM 2 12B Chat12B | Q4_K_M | 8.35 GB | +1.7 GB | 72–95Derived |
| Llama 3.2 11B Vision Instruct11B | Q4_K_M | 7.66 GB | +2.3 GB | 79–105Derived |
| Solar 10.7B Instruct11B | Q4_K_M | 7.6 GB | +2.4 GB | 82–108Derived |
| Falcon 3 10B Instruct10B | Q4_K_M | 7.28 GB | +2.7 GB | 84–111Derived |
| Gemma 2 9B Instruct9B | Q4_K_M | 7.3 GB | +2.7 GB | 94–125Derived |
| Qwen3.5 9B9B | Q4_K_M | 6.58 GB | +3.4 GB | 86–113Derived |
7B · 25
| Model | Quant | Est. VRAM | Headroom | tok/s |
|---|---|---|---|---|
| GLM-4-9B-Chat9B | Q4_K_M | 5.87 GB | +4.1 GB | 97–128Derived |
| Qwen3-VL 8B Instruct8B | Q5_K_M | 7.15 GB | +2.8 GB | 85–112Derived |
| Granite 4.2 8B8B | Q4_K_M | 6.28 GB | +3.7 GB | 99–131Derived |
| Ministral 3 8B Instruct8B | Q5_K_M | 6.92 GB | +3.1 GB | 87–115Derived |
| Qwen2-VL 7B Instruct7B | Q4_K_M | 5.49 GB | +4.5 GB | 105–139Derived |
| Granite 3.1 8B Instruct8B | Q4_K_M | 5.88 GB | +4.1 GB | 106–141Derived |
| Qwen3 8B Instruct8B | Q6_K | 7.64 GB | +2.4 GB | 79–104Derived |
| Llama 3.1 8B Instruct8B | Q6_K | 7.43 GB | +2.6 GB | 80–106Derived |
| Nous Hermes 3 Llama 3.1 8B8B | EXL2 4.65bpw | 5.43 GB | +4.6 GB | ≤172Ceiling |
| Aya 23 8B8B | Q4_K_M | 5.64 GB | +4.4 GB | 109–144Derived |
| OpenChat 3.6 8B8B | EXL2 4.65bpw | 5.43 GB | +4.6 GB | ≤172Ceiling |
| DeepSeek-R1-Distill-Llama-8B8B | Q5_K_M | 6.51 GB | +3.5 GB | 93–123Derived |
| InternLM2 7B Chat7B | Q4_K_M | 5.45 GB | +4.6 GB | 113–149Derived |
| Qwen2.5 7B Instruct7B | Q6_K | 6.77 GB | +3.2 GB | 85–112Derived |
| Qwen2.5-Coder 7B Instruct7B | EXL2 4.65bpw | 4.87 GB | +5.1 GB | ≤181Ceiling |
| DeepSeek-R1-Distill-Qwen-7B7B | EXL2 4.65bpw | 4.87 GB | +5.1 GB | ≤181Ceiling |
| Gemma 4 E4B ITE4B | Q8_0 | 8.46 GB | +1.5 GB | 66–88Derived |
| OLMo 2 7B Instruct7B | Q4_K_M | 6.82 GB | +3.2 GB | 120–158Derived |
| OLMo 3 7B Instruct7B | Q4_K_M | 6.82 GB | +3.2 GB | 120–158Derived |
| WizardLM-2 7B7B | Q4_K_M | 5.14 GB | +4.9 GB | 120–159Derived |
| Mistral 7B Instruct v0.37B | Q6_K | 6.75 GB | +3.3 GB | 89–118Derived |
| Zephyr 7B Beta7B | Q6_K | 6.75 GB | +3.3 GB | 89–118Derived |
| Gemma 4 E2B ITE2B | Q8_0 | 5.2 GB | +4.8 GB | ≤162Ceiling |
| Qwen3.5 4B4B | Q8_0 | 4.97 GB | +5 GB | ≤173Ceiling |
| Gemma 3 4B IT4B | Q8_0 | 5.05 GB | +5 GB | ≤175Ceiling |
≤3B · 14
| Model | Quant | Est. VRAM | Headroom | tok/s |
|---|---|---|---|---|
| Qwen3 4B Instruct4B | Q6_K | 4.05 GB | +6 GB | ≤244Ceiling |
| Phi-4 Mini Instruct3.8B | Q8_0 | 4.81 GB | +5.2 GB | ≤196Ceiling |
| Phi-3.5 Mini Instruct3.8B | Q8_0 | 5.89 GB | +4.1 GB | ≤197Ceiling |
| Granite 4.2 3B3B | Q8_0 | 4.41 GB | +5.6 GB | ≤206Ceiling |
| Llama 3.2 3B Instruct3B | Q8_0 | 4.05 GB | +6 GB | ≤235Ceiling |
| Qwen2.5 3B Instruct3B | Q8_0 | 3.59 GB | +6.4 GB | ≤244Ceiling |
| SmolLM3 3B3B | Q8_0 | 3.73 GB | +6.3 GB | ≤244Ceiling |
| Gemma 2 2B Instruct2B | Q8_0 | 3.34 GB | +6.7 GB | ≤289Ceiling |
| Qwen3.5 2B2B | Q8_0 | 2.23 GB | +7.8 GB | ≤384Ceiling |
| Qwen3 1.7B Instruct1.7B | Q8_0 | 2.39 GB | +7.6 GB | ≤437Ceiling |
| Qwen2.5 1.5B Instruct1.5B | Q8_0 | 1.83 GB | +8.2 GB | ≤490Ceiling |
| Llama 3.2 1B Instruct1B | Q8_0 | 1.51 GB | +8.5 GB | ≤608Ceiling |
| Qwen3.5 0.8B0.8B | Q8_0 | 0.94 GB | +9.1 GB | ≤938Ceiling |
| Qwen2.5 0.5B Instruct0.5B | Q8_0 | 0.6 GB | +9.4 GB | ≤1551Ceiling |
How this list is built
Each row is the lowest-perplexity-loss quant of that model whose estimated total — weights plus KV cache at 4K context plus activation buffer — uses at most 88% of the card. That is the calculator's "green" threshold, so every row here has real headroom rather than only just fitting. Raise the context length and the list shortens; the calculator lets you check any combination directly. The speed column is this card: a measured run, a range scaled from this card's own Llama 2 7B benchmark, or a bandwidth ceiling. A figure that only exists for the RTX 4090 is labelled as that card.
Measured on this card
No benchmark runs in this index were recorded on a RTX 3080 10G. Every figure on this page is calculated from the model architecture and the quant level — treat them as estimates, not measurements.
Stepping up
A RTX 5080 (16GB) fits 7 more of the indexed models than this card. RTX 5080 →
Before buying, hear the speed difference on a model both cards hold: RTX 3080 10G vs RTX 5080 (Mistral Nemo 12B Instruct) →
Common questions
What is the best local LLM for an RTX 3080 10G?
For everyday use, GLM-4-9B-Chat at Q4_K_M — about 5.9 GB of the card's 10 GB at 4K context, leaving 4.1 GB for a longer window. If you want the largest thing that will load, that is DeepSeek-Coder-V2-Lite Instruct at AWQ INT4 (8.3 GB, 1.7 GB spare). "Best" here means best fit for the memory budget — this index does not run task benchmarks, so it cannot tell you which model is smarter.
Can an RTX 3080 10G run InternLM2 20B Chat?
No. Its smallest build here, Q4_K_M, needs about 13.4 GB and this card has 10.0 GB usable — short by 3.4 GB before any context beyond 4K. The largest model this card does clear is DeepSeek-Coder-V2-Lite Instruct.
How many tokens per second does an RTX 3080 10G do on an 8B model at Q4?
This index has no measured run on this card, so it does not publish a figure. What can be stated from specifications: generating a token requires reading every weight once, Llama 3.1 8B Instruct at Q4_K_M is 4.6 GB of weights, and this card moves 760 GB/s — a ceiling near 165 tok/s. Batch size, context length, the runtime and how much of the model sits in cache all take you below it.
RTX 3080 10G or RTX 5070 for local LLMs?
They hold the same models: 10 GB against 12 GB fits 56 and 56 of 110 respectively at 4K. The difference is throughput — 760 GB/s against 672 GB/s, a 1.1× gap in how fast the weights can be read, which is what token generation is bound by. The RTX 3080 10G generates faster on any model both can hold.
56 of 110 indexed models fit comfortably in 10GB at 4K context, each at the highest-quality quant that still leaves headroom.
This page's figures change when the model or the runtime does.Last updated 2026-10-18 RSS → /feed.xml