FitMyLLM
NVIDIA· MAXWELL

NVIDIA GeForce 930A

Running LLMs on the GeForce 930A — 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
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 GB/s
FP16 COMPUTE
0.7 TFLOPS
TDP
33W
MEMORY
DDR3
ARCHITECTURE
Maxwell
CUDA CORES
384
PCIE
Gen 3 x8
13
FAST MODELS · >30 TOK/S
Real-time chat speed
36
USABLE · >10 TOK/S
Comfortable for all tasks
47
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test GeForce 930A (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
142
TOK/S · 27% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
95
TOK/S · 29% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
91
TOK/S · 29% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
91
TOK/S · 29% VRAM
A
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
50
TOK/S · 32% VRAM
A
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
47
TOK/S · 33% VRAM
A
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
42
TOK/S · 34% VRAM
B
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
38
TOK/S · 35% VRAM
B
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
38
TOK/S · 35% VRAM
B
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
38
TOK/S · 35% VRAM
B
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
37
TOK/S · 35% VRAM
B
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
36
TOK/S · 35% VRAM
B
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
34
TOK/S · 36% VRAM
B
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
26
TOK/S · 40% VRAM
B
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
26
TOK/S · 40% VRAM
B
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
25
TOK/S · 40% VRAM
C
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
23
TOK/S · 42% VRAM
C
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
23
TOK/S · 42% VRAM
C
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
21
TOK/S · 43% VRAM
C
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
21
TOK/S · 43% VRAM
C
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
21
TOK/S · 43% VRAM
C
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
21
TOK/S · 43% VRAM
C
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
17
TOK/S · 47% VRAM
C
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
17
TOK/S · 47% VRAM
C
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
16
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
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
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
D
Qwen 2.5 1.5B1.5B
QWEN·32K CTX· CHAT· CODING
9
TOK/S · 70% VRAM
D
Yi Coder 1.5B1.5B
YI·125K CTX· CODING
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 930A.

WHAT THIS CARD IS WORTH

930A 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 GB10 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB10 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB11 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB9 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
930A2 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 930A — 2 GB VRAM.

GEFORCE 930A SPEC
BRAND
NVIDIA
VRAM
2 GB DDR3
BANDWIDTH
16 GB/s
FP16 COMPUTE
0.7 TFLOPS
FP32 COMPUTE
0.7 TFLOPS
CUDA CORES
384
TDP
33 W
ARCHITECTURE
Maxwell
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 16 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 930A
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 GB843.8
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 GB916.8
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