FitMyLLM
▸ NVIDIA· AMPERE

NVIDIA RTX 3080 Ti

Running LLMs on the RTX 3080 Ti — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $550 buys

VRAM
12 GB
ENTRY-LEVEL
BANDWIDTH
912
GB/S
MODELS Q4
248/449
55%
7B Q4 SPEED
~104
BLAZING
▸ MODEL COVERAGE @ Q455% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~243
TOK/S
7B
~104
TOK/S
14B
~52
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
12 GB
BANDWIDTH
912 GB/s
FP16 COMPUTE
68 TFLOPS
TDP
350W
MEMORY
GDDR6X
ARCHITECTURE
Ampere
CUDA CORES
10,240
TENSOR CORES
320
PCIE
Gen 4 x16
MSRP
$550
248
FAST MODELS · >30 TOK/S
Real-time chat speed
248
USABLE · >10 TOK/S
Comfortable for all tasks
248
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

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

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▸ COMPATIBLE MODELS· 248
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
8107
TOK/S · 5% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
5404
TOK/S · 5% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
5211
TOK/S · 5% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
5211
TOK/S · 5% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
2850
TOK/S · 5% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
2702
TOK/S · 5% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2392
TOK/S · 6% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
2178
TOK/S · 6% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
2178
TOK/S · 6% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
2178
TOK/S · 6% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
2085
TOK/S · 6% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
2027
TOK/S · 6% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1920
TOK/S · 6% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
1459
TOK/S · 7% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
1459
TOK/S · 7% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
1403
TOK/S · 7% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
1285
TOK/S · 7% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1285
TOK/S · 7% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
1216
TOK/S · 7% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
1216
TOK/S · 7% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
1216
TOK/S · 7% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
1177
TOK/S · 7% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
986
TOK/S · 8% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
973
TOK/S · 8% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
901
TOK/S · 8% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
839
TOK/S · 9% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
776
TOK/S · 9% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
676
TOK/S · 10% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
663
TOK/S · 10% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
608
TOK/S · 10% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
608
TOK/S · 10% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
608
TOK/S · 10% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
608
TOK/S · 10% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
561
TOK/S · 11% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
561
TOK/S · 11% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
561
TOK/S · 11% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
540
TOK/S · 11% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
514
TOK/S · 11% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
514
TOK/S · 11% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
486
TOK/S · 46% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

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

WHAT THIS CARD IS WORTH

RTX 3080 Ti holds 248 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Ling-lite 16.8B at IQ4_XS.

TOKENS/SEC PER $100
16.0
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
2.18 GB
what memory costs on this card
THE BIGGEST IT TAKES
Ling-lite 16.8B16.8B · IQ4_XS10.7 GB297 tok/sEST
DeepSeek V2 Lite 16B16B · Q4_K_S10.6 GB284 tok/sEST
StarCoder2 15B15.96B · Q4_K_S10.7 GB43 tok/sEST
DeepSeek-Coder-V2-Lite 15.7B15.7B · Q4_K_S10.4 GB284 tok/sEST
DeepSeek R1 Distill Qwen 14B14.8B · Q4_K_S10.4 GB46 tok/sEST
DeepCoder 14B14.8B · Q4_K_S10.4 GB46 tok/sEST
Qwen2.5-Coder-14B14.8B · Q4_K_S10.4 GB46 tok/sEST
Qwen2.5-14B14.8B · Q4_K_S10.4 GB46 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M4 Pro (24GB)16 GB$1,3992.5 tok/s per $100
M4 (24GB)16 GB$6992.9 tok/s per $100
M2 (24GB)16 GB$9991.7 tok/s per $100
M3 (24GB)16 GB$9991.7 tok/s per $100
RTX 3080 Ti12 GB$55016.0 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 Ti — 12 GB VRAM.

▸ RTX 3080 TI SPEC
BRAND
NVIDIA
VRAM
12 GB GDDR6X
BANDWIDTH
912 GB/s
FP16 COMPUTE
68 TFLOPS
FP32 COMPUTE
34.1 TFLOPS
CUDA CORES
10,240
TENSOR CORES
320
TDP
350 W
ARCHITECTURE
Ampere
MSRP
$550
▸ AI CAPABILITY
248/ 449 models @ Q4

With 12 GB VRAM and 912 GB/s bandwidth, this GPU handles models up to 14.8B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~104 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR RTX 3080 TI
248 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
DeepSeek R1 Distill Qwen 14B14.8B9.5 GB4943.9
DeepCoder 14B14.8B9.5 GB4938.7
Qwen2.5-Coder-14B14.8B9.5 GB4941.3
Qwen2.5-14B14.8B9.5 GB4941.3
Qwen3 14B14.8B9.5 GB4945.7
phi-4 14B14.66B9.4 GB5033.7
Phi-4-reasoning 14B14.66B9.4 GB5033.7
Phi-4-reasoning-plus 14B14.66B9.4 GB5075.5
Phi-3-medium-14b14B9.0 GB5233.7
Qwen 1.5 14B14B9.0 GB5241.3
Ministral 3 14B Reasoning13.95B9.0 GB52—
Baichuan2 13B13B8.4 GB5623.6
Llama 2 13B13B8.4 GB5617.2
CodeLlama 13B13B8.4 GB5619.7
Vicuna 13B13B8.4 GB5611.8
LLaMA 1 13B13B8.4 GB5632.9
OPT 13B13B8.4 GB5635.8
Orca 2 13B13B8.4 GB5625.4
WizardCoder Python 13B13B8.4 GB5660.1
WizardLM 13B13B8.4 GB5619.5