FitMyLLM
NVIDIA· PASCAL

NVIDIA GeForce GT 1030 DDR4

Running LLMs on the GeForce GT 1030 DDR4 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
16.8
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~2
SLOW
▸ 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
16.8 GB/s
FP16 COMPUTE
2.2 TFLOPS
TDP
20W
MEMORY
DDR4
ARCHITECTURE
Pascal
CUDA CORES
384
PCIE
Gen 3 x4
13
FAST MODELS · >30 TOK/S
Real-time chat speed
37
USABLE · >10 TOK/S
Comfortable for all tasks
47
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test GeForce GT 1030 DDR4 (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 47
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
149
TOK/S · 27% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
100
TOK/S · 29% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
96
TOK/S · 29% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
96
TOK/S · 29% VRAM
A
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
53
TOK/S · 32% VRAM
A
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
50
TOK/S · 33% VRAM
A
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
44
TOK/S · 34% VRAM
A
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
40
TOK/S · 35% VRAM
A
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
40
TOK/S · 35% VRAM
A
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
40
TOK/S · 35% VRAM
B
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
38
TOK/S · 35% VRAM
B
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
37
TOK/S · 35% VRAM
B
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
35
TOK/S · 36% VRAM
B
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
27
TOK/S · 40% VRAM
B
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
27
TOK/S · 40% VRAM
B
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
26
TOK/S · 40% VRAM
C
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
24
TOK/S · 42% VRAM
C
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
24
TOK/S · 42% VRAM
C
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
22
TOK/S · 43% VRAM
C
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
22
TOK/S · 43% VRAM
C
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
22
TOK/S · 43% VRAM
C
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
22
TOK/S · 43% VRAM
C
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
18
TOK/S · 47% VRAM
C
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
18
TOK/S · 47% VRAM
C
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
17
TOK/S · 49% VRAM
C
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
15
TOK/S · 51% VRAM
D
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
14
TOK/S · 53% VRAM
D
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
12
TOK/S · 58% VRAM
D
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
12
TOK/S · 57% VRAM
D
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
11
TOK/S · 61% VRAM
D
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
11
TOK/S · 61% VRAM
D
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
11
TOK/S · 61% VRAM
D
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
11
TOK/S · 61% VRAM
D
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
10
TOK/S · 66% VRAM
D
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
10
TOK/S · 64% VRAM
D
OPT 1.3B1.3B
OPT·2K CTX· CHAT
10
TOK/S · 64% VRAM
D
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
10
TOK/S · 64% VRAM
D
Falcon-H1 1.5B1.55B
FALCON·128K CTX· CHAT· CODING
9
TOK/S · 72% VRAM
D
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
9
TOK/S · 70% VRAM
D
Qwen2 Math 1.5B1.5B
QWEN·4K CTX· REASONING
9
TOK/S · 70% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your GeForce GT 1030 DDR4.

WHAT THIS CARD IS WORTH

GT 1030 DDR4 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 GB11 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB11 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB11 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB10 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB10 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB10 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB10 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB10 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
GT 1030 DDR42 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 GT 1030 DDR4 — 2 GB VRAM.

GEFORCE GT 1030 DDR4 SPEC
BRAND
NVIDIA
VRAM
2 GB DDR4
BANDWIDTH
16.8 GB/s
FP16 COMPUTE
2.2 TFLOPS
FP32 COMPUTE
1.1 TFLOPS
CUDA CORES
384
TDP
20 W
ARCHITECTURE
Pascal
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 16.8 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~2 tok/s.

§ 01TOP MODELS FOR GEFORCE GT 1030 DDR4
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB85.1
stablelm-2-1_6b1.6B1.5 GB89.5
Falcon-H1 1.5B1.55B1.4 GB943.8
Qwen2.5-Coder-1.5B1.5B1.4 GB919.6
Qwen2 Math 1.5B1.5B1.4 GB919.6
Qwen 2.5 1.5B1.5B1.4 GB930.2
Yi Coder 1.5B1.5B1.4 GB914.6
Stella en 1.5B v51.5B1.4 GB9
Phi-1 1.3B1.42B1.4 GB97.2
Phi-1.5 1.3B1.42B1.4 GB97.2
DeepSeek Coder 1.3B1.35B1.3 GB1016.8
EXAONE-4.0-1.2B1.3B1.3 GB1018.9
OPT 1.3B1.3B1.3 GB105.3
MiniCPM-V 4.61.3B1.3 GB1021.5
LFM2.5-1.2B-Thinking1.2B1.2 GB1119.6
Llama-3.2-1B1.2B1.2 GB1110.1
LFM2 1.2B1.2B1.2 GB1115.6
Zamba2 1.2B1.2B1.2 GB1141.5
TinyLlama 1.1B1.1B1.2 GB1213.6
MiniCPM5 1B1.08B1.1 GB1226.5