FitMyLLM
▸ NVIDIA· TURING

NVIDIA RTX 2080 Ti

Running LLMs on the RTX 2080 Ti — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $350 buys

VRAM
11 GB
ENTRY-LEVEL
BANDWIDTH
616
GB/S
MODELS Q4
234/449
52%
7B Q4 SPEED
~70
BLAZING
▸ MODEL COVERAGE @ Q452% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~164
TOK/S
7B
~70
TOK/S
14B
~35
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
11 GB
BANDWIDTH
616 GB/s
FP16 COMPUTE
53.8 TFLOPS
TDP
250W
MEMORY
GDDR6
ARCHITECTURE
Turing
CUDA CORES
4,352
TENSOR CORES
544
PCIE
Gen 3 x16
MSRP
$350
234
FAST MODELS · >30 TOK/S
Real-time chat speed
234
USABLE · >10 TOK/S
Comfortable for all tasks
234
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test RTX 2080 Ti (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 234
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
5476
TOK/S · 5% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
3650
TOK/S · 5% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
3520
TOK/S · 5% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
3520
TOK/S · 5% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
1925
TOK/S · 6% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
1825
TOK/S · 6% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1616
TOK/S · 6% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
1471
TOK/S · 6% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
1471
TOK/S · 6% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
1471
TOK/S · 6% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
1408
TOK/S · 6% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
1369
TOK/S · 6% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1297
TOK/S · 7% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
986
TOK/S · 7% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
986
TOK/S · 7% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
948
TOK/S · 7% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
868
TOK/S · 8% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
868
TOK/S · 8% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
821
TOK/S · 8% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
821
TOK/S · 8% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
821
TOK/S · 8% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
795
TOK/S · 8% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
666
TOK/S · 9% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
657
TOK/S · 9% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
608
TOK/S · 9% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
566
TOK/S · 9% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
524
TOK/S · 10% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
456
TOK/S · 10% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
448
TOK/S · 11% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
411
TOK/S · 11% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
411
TOK/S · 11% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
411
TOK/S · 11% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
411
TOK/S · 11% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
379
TOK/S · 12% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
379
TOK/S · 12% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
379
TOK/S · 12% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
365
TOK/S · 12% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
347
TOK/S · 12% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
347
TOK/S · 12% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
329
TOK/S · 51% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your RTX 2080 Ti.

WHAT THIS CARD IS WORTH

RTX 2080 Ti holds 234 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Qwen3 14B at IQ4_XS.

TOKENS/SEC PER $100
18.3
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
3.14 GB
what memory costs on this card
THE BIGGEST IT TAKES
Qwen3 14B14.8B · IQ4_XS9.9 GB35 tok/sEST
Phi-3-medium-14b14B · Q4_K_S9.8 GB35 tok/sEST
Ministral 3 14B Reasoning13.95B · Q4_K_S9.7 GB35 tok/sEST
Mistral-Nemo 12.2B12.2B · Q4_K_M9.0 GB39 tok/sEST
Dolly v2 12B12B · Q4_K_M9.1 GB39 tok/sEST
gemma-3-12b12B · IQ4_XS9.8 GB43 tok/sEST
Pixtral 12B12B · IQ4_XS9.8 GB43 tok/sEST
StableLM 2 12B12B · Q4_K_M9.0 GB39 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 2080 Ti11 GB$35018.3 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 2080 Ti — 11 GB VRAM.

▸ RTX 2080 TI SPEC
BRAND
NVIDIA
VRAM
11 GB GDDR6
BANDWIDTH
616 GB/s
FP16 COMPUTE
53.8 TFLOPS
FP32 COMPUTE
13.4 TFLOPS
CUDA CORES
4,352
TENSOR CORES
544
TDP
250 W
ARCHITECTURE
Turing
MSRP
$350
▸ AI CAPABILITY
234/ 449 models @ Q4

With 11 GB VRAM and 616 GB/s bandwidth, this GPU handles models up to 13B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~70 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR RTX 2080 TI
234 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Baichuan2 13B13B8.4 GB3823.6
Llama 2 13B13B8.4 GB3817.2
CodeLlama 13B13B8.4 GB3819.7
Vicuna 13B13B8.4 GB3811.8
LLaMA 1 13B13B8.4 GB3832.9
OPT 13B13B8.4 GB3835.8
Orca 2 13B13B8.4 GB3825.4
WizardCoder Python 13B13B8.4 GB3860.1
WizardLM 13B13B8.4 GB3819.5
Mistral-Nemo 12.2B12.2B7.9 GB4022.4
Dolly v2 12B12B7.8 GB416.4
StableLM 2 12B12B7.8 GB4121.3
Falcon2 11B11B7.2 GB4533.2
Llama-3.2-11B-Vision-Instruct11B7.2 GB4534.4
SOLAR-10.7B10.7B7.0 GB4628.2
Falcon3-10B10.3B6.8 GB4838.2
GLM-4.1V 9B Thinking10.29B6.8 GB48—
Bamba 9B v29.78B6.5 GB5026.1
Qwen 3.5 9B9.65B6.4 GB5150.6
RecurrentGemma 9B9.63B6.4 GB5135.0