FitMyLLM
NVIDIA· AMPERE

NVIDIA RTX 3080 10GB

Running LLMs on the RTX 3080 10GB — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $429 buys

VRAM
10 GB
ENTRY-LEVEL
BANDWIDTH
760
GB/S
MODELS Q4
223/449
50%
7B Q4 SPEED
~87
BLAZING
▸ MODEL COVERAGE @ Q450% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

Average speeds at Q4 quantization. Actual performance varies by model architecture and context length.

3B
~203
TOK/S
7B
~87
TOK/S
14B
~43
TOK/S
32B
18.0GB NEEDED
70B
39.4GB NEEDED
▸ MEASURED RIG REPORTS

We rent the machine and time every model on it: decode, VRAM peak, concurrency, watts and cost per million tokens. Yours may already be one of them — and two are free to read in full.

SEE THE REPORTS
▸ SPECIFICATIONS
VRAM
10 GB
BANDWIDTH
760 GB/s
FP16 COMPUTE
60 TFLOPS
TDP
320W
MEMORY
GDDR6X
ARCHITECTURE
Ampere
CUDA CORES
8,704
TENSOR CORES
272
PCIE
Gen 4 x16
MSRP
$429
223
FAST MODELS · >30 TOK/S
Real-time chat speed
223
USABLE · >10 TOK/S
Comfortable for all tasks
223
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test RTX 3080 10GB (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 223
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
6756
TOK/S · 5% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
4504
TOK/S · 6% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
4343
TOK/S · 6% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
4343
TOK/S · 6% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
2375
TOK/S · 6% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
2252
TOK/S · 7% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1993
TOK/S · 7% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
1815
TOK/S · 7% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
1815
TOK/S · 7% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
1815
TOK/S · 7% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
1737
TOK/S · 7% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
1689
TOK/S · 7% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1600
TOK/S · 7% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
1216
TOK/S · 8% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
1216
TOK/S · 8% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
1169
TOK/S · 8% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
1070
TOK/S · 8% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1070
TOK/S · 8% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
1013
TOK/S · 9% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
1013
TOK/S · 9% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
1013
TOK/S · 9% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
981
TOK/S · 9% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
822
TOK/S · 9% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
811
TOK/S · 9% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
751
TOK/S · 10% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
699
TOK/S · 10% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
647
TOK/S · 11% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
563
TOK/S · 11% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
553
TOK/S · 12% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
507
TOK/S · 12% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
507
TOK/S · 12% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
507
TOK/S · 12% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
507
TOK/S · 12% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
468
TOK/S · 13% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
468
TOK/S · 13% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
468
TOK/S · 13% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
450
TOK/S · 13% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
428
TOK/S · 14% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
428
TOK/S · 14% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
405
TOK/S · 56% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your RTX 3080 10GB.

WHAT THIS CARD IS WORTH

RTX 3080 10GB holds 223 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Mistral-Nemo 12.2B at Q4_K_M.

TOKENS/SEC PER $100
17.7
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
2.33 GB
what memory costs on this card
THE BIGGEST IT TAKES
Mistral-Nemo 12.2B12.2B · Q4_K_M9.0 GB46 tok/sEST
Dolly v2 12B12B · Q4_K_S8.7 GB49 tok/sEST
StableLM 2 12B12B · Q4_K_M9.0 GB47 tok/sEST
Falcon2 11B11B · Q4_K_M8.5 GB51 tok/sEST
SOLAR-10.7B10.7B · Q4_K_M8.1 GB52 tok/sEST
Falcon3-10B10.3B · Q5_K_M8.8 GB47 tok/sEST
GLM-4.1V 9B Thinking10.29B · Q4_K_M8.9 GB54 tok/sEST
Bamba 9B v29.78B · Q5_K_M8.3 GB49 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M3 Pro (18GB)12 GB$1,5991.5 tok/s per $100
M1 Pro (16GB)11 GB$9992.9 tok/s per $100
M2 Pro (16GB)11 GB$1,2992.2 tok/s per $100
M4 (16GB)11 GB$4994.0 tok/s per $100
RTX 3080 10GB10 GB$42917.7 tok/s per $100

Neighbours in memory rather than in price: memory decides whether a card can do the job at all, so two cards of the same size at different prices is the comparison you are making. Ordered by memory, then bandwidth — not by the value column, which is worked out from bandwidth and price alone and therefore rewards a cheap card whatever its software stack does to that bandwidth in practice. Read it as one input, not as a ranking.

▸ DEVICE UNDER TEST

NVIDIA RTX 3080 10GB — 10 GB VRAM.

RTX 3080 10GB SPEC
BRAND
NVIDIA
VRAM
10 GB GDDR6X
BANDWIDTH
760 GB/s
FP16 COMPUTE
60 TFLOPS
FP32 COMPUTE
29.8 TFLOPS
CUDA CORES
8,704
TENSOR CORES
272
TDP
320 W
ARCHITECTURE
Ampere
MSRP
$429
▸ AI CAPABILITY
223/ 449 models @ Q4

With 10 GB VRAM and 760 GB/s bandwidth, this GPU handles models up to 12.2B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~87 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR RTX 3080 10GB
223 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Mistral-Nemo 12.2B12.2B7.9 GB5022.4
Dolly v2 12B12B7.8 GB516.4
StableLM 2 12B12B7.8 GB5121.3
Falcon2 11B11B7.2 GB5533.2
SOLAR-10.7B10.7B7.0 GB5728.2
Falcon3-10B10.3B6.8 GB5938.2
Bamba 9B v29.78B6.5 GB6226.1
Qwen 3.5 9B9.65B6.4 GB6350.6
RecurrentGemma 9B9.63B6.4 GB6335.0
glm-4-9b9.4B6.2 GB6520.5
MiniCPM-o 4.59.37B6.2 GB65
gemma-2-9b9.2B6.1 GB6630.2
Yi 1.5 9B9B6.0 GB6830.3
Yi Coder 9B9B6.0 GB6835.8
Ministral 3 8B8.92B5.9 GB6825.7
Ministral 3 8B Reasoning8.92B5.9 GB68
NVIDIA-Nemotron-Nano-9B-v28.9B5.9 GB6844.2
InternLM3 8B Instruct8.8B5.9 GB6938.7
Qwen3-VL 8B Instruct8.77B5.8 GB6926.4
MiniCPM-V 4.58.7B5.8 GB7026.1