FitMyLLM
NVIDIA· PASCAL

NVIDIA GeForce GTX 1050

Running LLMs on the GeForce GTX 1050 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
112
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~13
USABLE
▸ MODEL COVERAGE @ Q410% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
1.7GB NEEDED
7B
3.9GB NEEDED
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
2 GB
BANDWIDTH
112 GB/s
FP16 COMPUTE
1.9 TFLOPS
TDP
75W
MEMORY
GDDR5
ARCHITECTURE
Pascal
CUDA CORES
640
PCIE
Gen 3 x16
47
FAST MODELS · >30 TOK/S
Real-time chat speed
47
USABLE · >10 TOK/S
Comfortable for all tasks
47
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test GeForce GTX 1050 (or anything bigger) without committing. Pay by the second, cancel anytime.

Spin up in ~60s. Pay by the second. Cancel anytime.

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▸ COMPATIBLE MODELS· 47
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
996
TOK/S · 27% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
664
TOK/S · 29% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
640
TOK/S · 29% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
640
TOK/S · 29% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
350
TOK/S · 32% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
332
TOK/S · 33% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
294
TOK/S · 34% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
267
TOK/S · 35% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
267
TOK/S · 35% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
267
TOK/S · 35% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
256
TOK/S · 35% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
249
TOK/S · 35% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
236
TOK/S · 36% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
179
TOK/S · 40% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
179
TOK/S · 40% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
172
TOK/S · 40% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
158
TOK/S · 42% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
158
TOK/S · 42% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
149
TOK/S · 43% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
149
TOK/S · 43% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
149
TOK/S · 43% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
145
TOK/S · 43% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
121
TOK/S · 47% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
119
TOK/S · 47% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
111
TOK/S · 49% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
103
TOK/S · 51% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
95
TOK/S · 53% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
83
TOK/S · 57% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
81
TOK/S · 58% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
75
TOK/S · 61% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
75
TOK/S · 61% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
75
TOK/S · 61% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
75
TOK/S · 61% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
69
TOK/S · 64% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
69
TOK/S · 64% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
69
TOK/S · 64% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
66
TOK/S · 66% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
63
TOK/S · 68% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
63
TOK/S · 68% VRAM
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
60
TOK/S · 70% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your GeForce GTX 1050.

WHAT THIS CARD IS WORTH

GTX 1050 holds 47 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Moondream2 1.9B 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
Moondream2 1.9B1.9B · Q4_K_M1.7 GB68 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB67 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB70 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB62 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB64 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB64 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB64 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB64 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M2 (8GB)5 GB$5992.8 tok/s per $100
M3 (8GB)5 GB$5992.8 tok/s per $100
M1 (8GB)5 GB$4992.2 tok/s per $100
RTX 2060 6GB6 GB$15026.7 tok/s per $100
GTX 10502 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 1050 — 2 GB VRAM.

GEFORCE GTX 1050 SPEC
BRAND
NVIDIA
VRAM
2 GB GDDR5
BANDWIDTH
112 GB/s
FP16 COMPUTE
1.9 TFLOPS
FP32 COMPUTE
1.9 TFLOPS
CUDA CORES
640
TDP
75 W
ARCHITECTURE
Pascal
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 112 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~13 tok/s.

§ 01TOP MODELS FOR GEFORCE GTX 1050
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB565.1
stablelm-2-1_6b1.6B1.5 GB569.5
Falcon-H1 1.5B1.55B1.4 GB5843.8
Qwen2.5-Coder-1.5B1.5B1.4 GB6019.6
Qwen2 Math 1.5B1.5B1.4 GB6019.6
Qwen 2.5 1.5B1.5B1.4 GB6030.2
Yi Coder 1.5B1.5B1.4 GB6014.6
Stella en 1.5B v51.5B1.4 GB60
Phi-1 1.3B1.42B1.4 GB637.2
Phi-1.5 1.3B1.42B1.4 GB637.2
DeepSeek Coder 1.3B1.35B1.3 GB6616.8
EXAONE-4.0-1.2B1.3B1.3 GB6918.9
OPT 1.3B1.3B1.3 GB695.3
MiniCPM-V 4.61.3B1.3 GB6921.5
LFM2.5-1.2B-Thinking1.2B1.2 GB7519.6
Llama-3.2-1B1.2B1.2 GB7510.1
LFM2 1.2B1.2B1.2 GB7515.6
Zamba2 1.2B1.2B1.2 GB7541.5
TinyLlama 1.1B1.1B1.2 GB8113.6
MiniCPM5 1B1.08B1.1 GB8326.5