Your rig

GeForce RTX 2080 Ti · 11 GB

32 GB system RAM · Turing · 616 GB/s · 9.7 GB usable VRAM
Edit hardware Export as JSON

Context length

KV cache at f16, 4K tokens

Quality floor
Q8_0 — near-lossless Q6_K Q5_K_M Q4_K_M — recommended Q3_K_M Q2_K — damaged
KV cache type
System RAM (GB)
Runtime
Ollamallama.cppLM StudiovLLM

4 of 5 runtimes drive this device.

Allow CPU offload Include partial-offload fits

Offloaded layers run at 60 GB/s host bandwidth — expect single-digit tokens per second.

Which one should I actually run?

Best models for your GeForce RTX 2080 Ti

Updated 21 Aug 2026 5 new this week, 14 this month

What do you want to do? Each row is the best-ranked model on that use case's shortlist that fits at Q8_0 and 4K. The ordering is editorial; the sizes and speeds are computed. How the shortlists work.

RecommendationModelWhyQuant · totalTok/s est.
Best overall Qwen3.5 4B
Alibaba · Apache 2.0
The 6–8 GB pick: a real assistant in 3.4 GB of weights. Q8_0 · 5.3 GB 81 Runs great
Best for coding Qwen3.5 4B
Alibaba · Apache 2.0
A coding agent in 3.4 GB — the 6–8 GB answer. Q8_0 · 5.3 GB 81 Runs great
Best reasoning Qwen3.5 4B
Alibaba · Apache 2.0
Step-by-step reasoning in 8 GB. Q8_0 · 5.3 GB 81 Runs great
Best for writing Qwen3.5 4B
Alibaba · Apache 2.0
Competent drafting in 8 GB. Q8_0 · 5.3 GB 81 Runs great
Best vision Qwen3.5 4B
Alibaba · Apache 2.0
Reads screenshots in 8 GB. Q8_0 · 5.3 GB 81 Runs great
Best for agents Qwen3.5 4B
Alibaba · Apache 2.0
Function calling in 8 GB. Q8_0 · 5.3 GB 81 Runs great
Best translation Qwen3.5 4B
Alibaba · Apache 2.0
Usable translation in 8 GB. Q8_0 · 5.3 GB 81 Runs great
Fastest good model LFM2.5 8B-A1B
Liquid AI · LFM Open License v1.0
1.5B active; designed for laptops without a GPU. Q8_0 · 9.0 GB 97 Tight fit
Best long context Qwen3.5 2B
Alibaba · Apache 2.0
Long documents on 4 GB. Q8_0 · 4.3 GB
at 128K context
166 Runs great

Everything that fits

Runs great

15 of 76 · fits with headroom for a longer prompt
ModelParamsQuantWeights +KV 4KTotal VRAM headroom Tok/sRuntime
Gemma 4 E2B 5.1B Q8_0 5.0 GB 0.04 GB 5.7 GB
4.0 GB free
74 Ollama Run
Qwen3.5 4B 4.66B Q8_0 4.6 GB 0.13 GB 5.3 GB
4.4 GB free
81 Ollama Run
Gemma 3 4B 4.3B Q8_0 4.3 GB 0.20 GB 5.1 GB
4.6 GB free
88 Ollama Run
Qwen3 4B 4.02B Q8_0 4.0 GB 0.56 GB 5.1 GB
4.6 GB free
94 Ollama Run
Ministral 3 3B 3.85B Q8_0 3.8 GB 0.41 GB 4.8 GB
4.9 GB free
98 Ollama Run
Phi-4-mini 3.8B 3.84B Q8_0 3.8 GB 0.50 GB 4.9 GB
4.8 GB free
98 Ollama Run
Granite 4.1 3B 3.4B Q8_0 3.4 GB 0.31 GB 4.3 GB
5.4 GB free
111 Ollama Run
Llama 3.2 3B Instruct 3.21B Q8_0 3.2 GB 0.44 GB 4.2 GB
5.5 GB free
117 Ollama Run
LFM2.5 2.6B New 2.7B Q8_0 2.7 GB 0.06 GB 3.3 GB
6.4 GB free
140 llama.cpp Run
Qwen3.5 2B 2.27B Q8_0 2.2 GB 0.05 GB 2.9 GB
6.8 GB free
166 Ollama Run
Qwen3 1.7B 1.72B Q8_0 1.7 GB 0.44 GB 2.7 GB
7.0 GB free
219 Ollama Run
Llama 3.2 1B Instruct 1.24B Q8_0 1.2 GB 0.13 GB 2.0 GB
7.7 GB free
304 Ollama Run
Gemma 3 1B 1.0B Q8_0 1.0 GB 0.03 GB 1.6 GB
8.1 GB free
377 Ollama Run
Qwen3.5 0.8B 0.87B Q8_0 0.9 GB 0.05 GB 1.5 GB
8.2 GB free
433 Ollama Run
Qwen3 0.6B 0.6B Q8_0 0.6 GB 0.44 GB 1.6 GB
8.1 GB free
628 Ollama Run

Runs — tight

9 models · fits at 4K, drop context or quant to go longer
ModelParamsQuantWeights +KV 4KTotal VRAM headroom Tok/sRuntime
LFM2.5 8B-A1B 8.47B MoE / 1.5B act Q8_0 8.4 GB 0.05 GB 9.0 GB
0.7 GB free
97 llama.cpp Run
Fara 7B 8.29B Q8_0 8.2 GB 0.22 GB 9.0 GB
0.7 GB free
45 llama.cpp Run
Qwen3 8B 8.19B Q8_0 8.1 GB 0.56 GB 9.3 GB
0.4 GB free
46 llama.cpp Run
Llama 3.1 8B Instruct 8.03B Q8_0 7.9 GB 0.50 GB 9.0 GB
0.7 GB free
47 llama.cpp Run
Gemma 4 E4B 8.0B Q8_0 7.9 GB 0.09 GB 8.6 GB
1.1 GB free
47 llama.cpp Run
Ling 3.0 Tiny 7.9B-A1.3B New 7.9B MoE / 1.3B act Q8_0 7.8 GB 0.03 GB 8.4 GB
1.3 GB free
111 llama.cpp Run
DeepSeek-R1-Distill-Qwen 7B 7.62B Q8_0 7.5 GB 0.22 GB 8.4 GB
1.3 GB free
49 llama.cpp Run
Qwen2.5-Coder 7B 7.62B Q8_0 7.5 GB 0.22 GB 8.4 GB
1.3 GB free
49 llama.cpp Run
Mistral 7B Instruct v0.3 7.25B Q8_0 7.2 GB 0.50 GB 8.3 GB
1.4 GB free
52 llama.cpp Run

Won't fit in VRAM

52 models · some are possible with CPU offload
ModelParamsQuantWeights +KV 4KTotal Over budget Tok/sRuntime
Qwen3.6 35B-A3B 35.9B MoE / 3.3B act Q8_0 35.5 GB 0.08 GB 36.2 GB +26.5 GB ~5.6 llama.cpp Why
Ornith 1.5 35B-A3B New this week 35.9B MoE / 3.3B act Q8_0 35.5 GB 0.08 GB 36.2 GB +26.5 GB ~5.6 llama.cpp Why
LLM-jp 4 33B Thinking New this week 33.2B Q8_0 32.9 GB 1.00 GB 34.5 GB +24.8 GB ~1.4 llama.cpp Why
Qwen3 32B 32.8B Q8_0 32.5 GB 1.00 GB 34.1 GB +24.4 GB ~1.4 llama.cpp Why
DeepSeek-R1-Distill-Qwen 32B 32.8B Q8_0 32.5 GB 1.00 GB 34.1 GB +24.4 GB ~1.4 llama.cpp Why
Qwen2.5-Coder 32B 32.8B Q8_0 32.5 GB 1.00 GB 34.1 GB +24.4 GB ~1.4 llama.cpp Why
Olmo 3.1 32B Instruct 32.2B Q8_0 31.9 GB 1.00 GB 33.5 GB +23.8 GB ~1.5 llama.cpp Why
Nemotron 3.5 Lightning 30B-A3B New 31.6B MoE / 3.2B act Q8_0 31.3 GB 0.02 GB 31.9 GB +22.2 GB ~6.0 llama.cpp Why
Gemma 4 31B 31.3B Q8_0 31.0 GB 1.41 GB 33.0 GB +23.3 GB ~1.5 llama.cpp Why
GLM-4.7-Flash 30B-A3B 31.2B MoE / 3B act Q8_0 30.9 GB 0.21 GB 31.7 GB +22.0 GB ~6.4 llama.cpp Why
Qwen3 30B-A3B 30.5B MoE / 3.3B act Q8_0 30.2 GB 0.38 GB 31.2 GB +21.5 GB ~5.8 llama.cpp Why
Qwen3 Coder 30B-A3B 30.5B MoE / 3.3B act Q8_0 30.2 GB 0.38 GB 31.2 GB +21.5 GB ~5.8 llama.cpp Why
Muse Glimmer 30B New 29.8B Q8_0 29.5 GB 0.13 GB 30.2 GB +20.5 GB ~1.7 llama.cpp Why
Granite 4.1 30B 28.9B Q8_0 28.6 GB 1.00 GB 30.2 GB +20.5 GB ~1.7 llama.cpp Why
Qwen3.8 27B New this week 27.8B Q8_0 27.5 GB 0.25 GB 28.4 GB +18.7 GB ~1.9 llama.cpp Why
Qwen3.6 27B 27.8B Q8_0 27.5 GB 0.25 GB 28.4 GB +18.7 GB ~1.9 llama.cpp Why
Gemma 3 27B 27.4B Q8_0 27.1 GB 0.72 GB 28.4 GB +18.7 GB ~1.9 llama.cpp Why
Gemma 4 26B-A4B 26.5B MoE / 3.8B act Q8_0 26.2 GB 0.35 GB 27.2 GB +17.5 GB ~5.3 llama.cpp Why
Devstral Small 2 24B 24B Q8_0 23.7 GB 0.63 GB 25.0 GB +15.3 GB ~2.3 llama.cpp Why
Mistral Small 3.2 24B 23.6B Q8_0 23.4 GB 0.63 GB 24.6 GB +14.9 GB ~2.3 llama.cpp Why
gpt-oss 20B 20.9B MoE / 3.6B act MXFP4 10.8 GB 0.10 GB 11.5 GB +1.8 GB ~30 llama.cpp Why
Qwen3 14B 14.8B Q8_0 14.6 GB 0.63 GB 15.9 GB +6.2 GB ~5.2 llama.cpp Why
DeepSeek-R1-Distill-Qwen 14B 14.8B Q8_0 14.6 GB 0.75 GB 16.0 GB +6.3 GB ~5.1 llama.cpp Why
Qwen2.5-Coder 14B 14.8B Q8_0 14.6 GB 0.75 GB 16.0 GB +6.3 GB ~5.1 llama.cpp Why
Phi-4 14B 14.7B Q8_0 14.5 GB 0.78 GB 15.9 GB +6.2 GB ~5.2 llama.cpp Why
Ministral 3 14B 13.9B Q8_0 13.8 GB 0.63 GB 15.0 GB +5.3 GB ~5.9 llama.cpp Why
Gemma 3 12B 12.2B Q8_0 12.1 GB 0.56 GB 13.2 GB +3.5 GB ~8.3 llama.cpp Why
Mistral NeMo 12B 12.2B Q8_0 12.1 GB 0.63 GB 13.3 GB +3.6 GB ~8.2 llama.cpp Why
Gemma 4 12B 12B Q8_0 11.9 GB 0.56 GB 13.0 GB +3.3 GB ~8.7 llama.cpp Why
Qwen3.5 9B 9.65B Q8_0 9.5 GB 0.13 GB 10.3 GB +0.6 GB ~25 llama.cpp Why
Ornith 1.5 9B New this week 9.41B Q8_0 9.3 GB 0.13 GB 10.0 GB +0.3 GB ~30 llama.cpp Why
Ministral 3 8B 8.92B Q8_0 8.8 GB 0.53 GB 10.0 GB +0.3 GB ~33 llama.cpp Why
Granite 4.1 8B 8.79B Q8_0 8.7 GB 0.63 GB 9.9 GB +0.2 GB ~35 llama.cpp Why
Olmo 3 7B Instruct 7.3B Q8_0 7.2 GB 2.00 GB 9.8 GB +0.1 GB ~45 llama.cpp Why
Kimi K3 2.8T-A104B New 2780B MoE / 104B act Q8_0 2750.9 GB 0.11 GB 2751.6 GB +2741.9 GB ~0.1 llama.cpp Why
Qwen3.8 2.4T-A95B New 2446B MoE / 95B act Q8_0 2420.4 GB 0.36 GB 2421.4 GB +2411.7 GB ~0.1 llama.cpp Why
DeepSeek V4 Pro 1.6T-A49B New 1650B MoE / 49B act Q8_0 1632.7 GB 0.27 GB 1633.6 GB +1623.9 GB ~0.3 llama.cpp Why
Kimi K2.6 1T-A32B 1027B MoE / 32B act Q8_0 1016.2 GB 0.27 GB 1017.1 GB +1007.4 GB ~0.4 llama.cpp Why
GLM-5.2 744B-A40B 753B MoE / 40B act Q8_0 745.1 GB 0.34 GB 746.1 GB +736.4 GB ~0.4 llama.cpp Why
DeepSeek-R1 671B 671B MoE / 37B act Q8_0 664.0 GB 0.27 GB 664.8 GB +655.1 GB ~0.4 llama.cpp Why
Qwen3.5 397B-A17B 403B MoE / 17B act Q8_0 398.8 GB 0.12 GB 399.5 GB +389.8 GB ~0.8 llama.cpp Why
Ornith 1.5 397B-A17B New this week 397B MoE / 17B act Q8_0 392.8 GB 0.12 GB 393.6 GB +383.9 GB ~0.8 llama.cpp Why
DeepSeek V4 Flash 284B-A13B New 284B MoE / 13B act Q8_0 281.0 GB 0.19 GB 281.8 GB +272.1 GB ~1.1 llama.cpp Why
Qwen3 235B-A22B 235B MoE / 22B act Q8_0 232.5 GB 0.73 GB 233.9 GB +224.2 GB ~0.7 llama.cpp Why
Qwen3.5 122B-A10B 125B MoE / 10B act Q8_0 123.7 GB 0.09 GB 124.4 GB +114.7 GB ~1.5 llama.cpp Why
Nemotron 3 Super 120B-A12B 124B MoE / 12B act Q8_0 122.7 GB 0.03 GB 123.3 GB +113.6 GB ~1.3 llama.cpp Why
Ling 3.0 Flash 124B-A5B New 124B MoE / 5.1B act Q8_0 122.7 GB 0.03 GB 123.3 GB +113.6 GB ~3.0 llama.cpp Why
Mistral Small 4 119B-A6B 119B MoE / 6.5B act Q8_0 117.8 GB 0.09 GB 118.4 GB +108.7 GB ~2.3 llama.cpp Why
gpt-oss 120B 117B MoE / 5.1B act MXFP4 60.5 GB 0.15 GB 61.3 GB +51.6 GB ~6.1 llama.cpp Why
Llama 4 Scout 109B-A17B 109B MoE / 17B act Q8_0 107.9 GB 0.75 GB 109.2 GB +99.5 GB ~0.9 llama.cpp Why
Llama 3.3 70B Instruct 70.6B Q8_0 69.9 GB 1.25 GB 71.7 GB +62.0 GB ~0.6 llama.cpp Why
DeepSeek-R1-Distill-Llama 70B 70.6B Q8_0 69.9 GB 1.25 GB 71.7 GB +62.0 GB ~0.6 llama.cpp Why

Totals = quantised weights + f16 KV cache at 4K tokens + 0.6 GB runtime overhead, against 9.7 GB usable VRAM (11 GB less display and driver reserve). Tokens per second are estimated from 616 GB/s peak memory bandwidth at batch 1 — not measured. Read the method.