FitMyLLM
NVIDIA· PASCAL

NVIDIA GeForce GTX 1060 Mobile

Running LLMs on the GeForce GTX 1060 Mobile — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
6 GB
BUDGET
BANDWIDTH
192
GB/S
MODELS Q4
130/449
29%
7B Q4 SPEED
~22
GOOD
▸ MODEL COVERAGE @ Q429% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~51
TOK/S
7B
~22
TOK/S
14B
7.9GB NEEDED
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
6 GB
BANDWIDTH
192 GB/s
FP16 COMPUTE
0.1 TFLOPS
TDP
80W
MEMORY
GDDR5
ARCHITECTURE
Pascal
CUDA CORES
1,280
PCIE
Gen 3 x16
108
FAST MODELS · >30 TOK/S
Real-time chat speed
130
USABLE · >10 TOK/S
Comfortable for all tasks
130
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test GeForce GTX 1060 Mobile (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 130
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
1707
TOK/S · 9% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1138
TOK/S · 10% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1097
TOK/S · 10% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1097
TOK/S · 10% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
600
TOK/S · 11% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
569
TOK/S · 11% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
504
TOK/S · 11% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
459
TOK/S · 12% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
459
TOK/S · 12% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
459
TOK/S · 12% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
439
TOK/S · 12% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
427
TOK/S · 12% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
404
TOK/S · 12% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
307
TOK/S · 13% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
307
TOK/S · 13% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
295
TOK/S · 13% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
270
TOK/S · 14% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
270
TOK/S · 14% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
256
TOK/S · 14% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
256
TOK/S · 14% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
256
TOK/S · 14% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
248
TOK/S · 14% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
208
TOK/S · 16% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
205
TOK/S · 16% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
190
TOK/S · 16% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
177
TOK/S · 17% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
163
TOK/S · 18% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
142
TOK/S · 19% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
140
TOK/S · 19% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
128
TOK/S · 20% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
128
TOK/S · 20% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
128
TOK/S · 20% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
128
TOK/S · 20% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
118
TOK/S · 21% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
118
TOK/S · 21% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
118
TOK/S · 21% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
114
TOK/S · 22% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
108
TOK/S · 23% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
108
TOK/S · 23% VRAM
S
granite-4.0-h-tiny 6.9B6.9BMoE
GRANITE·128K CTX· CHAT
102
TOK/S · 78% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your GeForce GTX 1060 Mobile.

WHAT THIS CARD IS WORTH

GTX 1060 Mobile holds 130 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Falcon-H1 7B at Q4_K_M.

TOKENS/SEC PER $100
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
what memory costs on this card
THE BIGGEST IT TAKES
Falcon-H1 7B7.59B · Q4_K_M5.4 GB25 tok/sEST
Falcon-H1R 7B7.59B · Q4_K_M5.4 GB25 tok/sEST
Falcon Mamba 7B7.27B · Q4_K_M5.0 GB26 tok/sEST
WizardLM 2 7B7B · Q4_K_M5.3 GB27 tok/sEST
StarCoder2 7B7B · Q4_K_M5.0 GB27 tok/sEST
Dolly v2 7B6.9B · Q4_K_M5.3 GB28 tok/sEST
granite-4.0-h-tiny 6.9B6.9B · Q4_K_M5.0 GB128 tok/sEST
ChatGLM2 6B6.24B · Q5_K_M5.1 GB26 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
RTX 3080 10GB10 GB$42917.7 tok/s per $100
Arc B57010 GB$2198.2 tok/s per $100
Radeon RX 670010 GB$29912.0 tok/s per $100
Radeon RX 6750 GRE 10 GB10 GB$22915.7 tok/s per $100
GTX 1060 Mobile6 GB

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 GeForce GTX 1060 Mobile — 6 GB VRAM.

GEFORCE GTX 1060 MOBILE SPEC
BRAND
NVIDIA
VRAM
6 GB GDDR5
BANDWIDTH
192 GB/s
FP16 COMPUTE
0.1 TFLOPS
FP32 COMPUTE
4.3 TFLOPS
CUDA CORES
1,280
TDP
80 W
ARCHITECTURE
Pascal
▸ AI CAPABILITY
130/ 449 models @ Q4

With 6 GB VRAM and 192 GB/s bandwidth, this GPU handles models up to 7B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~22 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR GEFORCE GTX 1060 MOBILE
130 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Alpaca 7B7B4.8 GB2227.7
Baichuan2 7B7B4.8 GB2221.5
Vicuna 7B7B4.8 GB2222.0
MPT-7B7B4.8 GB227.8
Orca 2 7B7B4.8 GB2226.1
WizardLM 2 7B7B4.8 GB2226.1
StarCoder2 7B7B4.8 GB2217.0
WizardCoder Python 7B7B4.8 GB2253.7
WizardLM 7B7B4.8 GB2215.5
OLMo 3.1 RLZero 7B Code7B4.8 GB2221.8
OLMo 3.1 RLZero 7B Math7B4.8 GB2221.8
Dolly v2 7B6.9B4.7 GB227.0
granite-4.0-h-tiny 6.9B6.9B4.7 GB10249.2
Llama 2 7B6.74B4.6 GB2321.1
CodeLlama 7B6.74B4.6 GB2328.1
LLaMA 1 7B6.74B4.6 GB2330.8
DeepSeek Coder 6.7B6.7B4.6 GB2323.6
OPT 6.7B6.7B4.6 GB2318.5
ChatGLM2 6B6.24B4.3 GB2520.7
ChatGLM3 6B6.24B4.3 GB2542.7