▸ NVIDIA· FERMI 2.0

NVIDIA GeForce GTX 460 v2 ES

Running LLMs on the GeForce GTX 460 v2 ES — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM

1 GB

BUDGET

BANDWIDTH

128.3

GB/S

MODELS Q4

16/449

7B Q4 SPEED

~15

GOOD

▸ MODEL COVERAGE @ Q44% OF ALL

▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

—

1.7GB NEEDED

—

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

1 GB

BANDWIDTH

128.3 GB/s

FP16 COMPUTE

1 TFLOPS

TDP

160W

MEMORY

GDDR5

ARCHITECTURE

Fermi 2.0

CUDA CORES

336

PCIE

Gen 2 x16

FAST MODELS · >30 TOK/S

Real-time chat speed

USABLE · >10 TOK/S

Comfortable for all tasks

TOTAL COMPATIBLE

Fit in VRAM at Q4

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▸ COMPATIBLE MODELS· 16

Falcon-H1R Tiny 90M0.09B

FALCON·256K CTX· CHAT· REASONING

nomic-embed-text-v1.5 100M0.14B

EMBEDDING·8K CTX· CHAT

SMOLLM·8K CTX· CHAT· VISION

Snowflake Arctic Embed M v2.00.305B

EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL

bge-large-en-v1.5 335M0.335B

EMBEDDING·1K CTX· CHAT

mxbai-embed-large-v10.335B

EMBEDDING·1K CTX· EMBEDDING

Snowflake Arctic Embed L0.335B

EMBEDDING·1K CTX· EMBEDDING

LFM·125K CTX· CHAT· TOOL_USE

GPT-2 Medium 345M0.38B

SMOLLM·8K CTX· CHAT· VISION

FALCON·128K CTX· CHAT

▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your GeForce GTX 460 v2 ES.

▸Find models for GeForce GTX 460 v2 ES Compare with other GPUs

WHAT THIS CARD IS WORTH

GTX 460 v2 ES holds 16 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is TinyLlama 1.1B 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

TinyLlama 1.1B1.1B · Q4_K_M1.1 GB130 tok/sEST

MiniCPM5 1B1.08B · Q4_K_M1.0 GB132 tok/sEST

InternVL3 1B0.94B · Q5_K_M1.0 GB130 tok/sEST

Qwen 3.5 0.8B0.87B · Q6_K1.1 GB122 tok/sEST

LFM2 700M0.74B · Q6_K1.0 GB144 tok/sEST

Qwen 1.5 0.5B0.62B · Q5_K_M1.1 GB198 tok/sEST

Qwen3-Embedding 0.6B0.6B · Q4_K_M1.1 GB238 tok/sEST

Falcon-H1R Tiny 0.6B0.6B · Q8_01.0 GB137 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

Radeon RX 6600S4 GB$175—

GTX 460 v2 ES1 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 460 v2 ES — 1 GB VRAM.

▸ GEFORCE GTX 460 V2 ES SPEC

BRAND: NVIDIA
VRAM: 1 GB GDDR5
BANDWIDTH: 128.3 GB/s
FP16 COMPUTE: 1 TFLOPS
FP32 COMPUTE: 1 TFLOPS
CUDA CORES: 336
TDP: 160 W
ARCHITECTURE: Fermi 2.0

▸ AI CAPABILITY

16/ 449 models @ Q4

With 1 GB VRAM and 128.3 GB/s bandwidth, this GPU handles models up to 0.52B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~15 tok/s.

§ 01TOP MODELS FOR GEFORCE GTX 460 V2 ES

16 FIT · SHOWING 16

MODEL	SIZE	VRAM Q4	TOK/S	AVG
Falcon-H1 0.5B	0.52B	0.8 GB	197	41.7
Qwen 2.5 0.5B	0.5B	0.8 GB	205	19.4
SmolVLM 500M	0.5B	0.8 GB	205	—
GPT-2 Medium 345M	0.38B	0.7 GB	270	5.9
SmolLM2 360M	0.36B	0.7 GB	285	8.2
LFM2 350M	0.35B	0.7 GB	293	46.3
bge-large-en-v1.5 335M	0.335B	0.7 GB	306	62.3
mxbai-embed-large-v1	0.335B	0.7 GB	306	64.7
Snowflake Arctic Embed L	0.335B	0.7 GB	306	56.0
Snowflake Arctic Embed M v2.0	0.305B	0.7 GB	337	—
Gemma 3 270M	0.27B	0.7 GB	380	12.6
SmolVLM 256M	0.256B	0.6 GB	401	28.3
nomic-embed-text-v1.5 100M	0.14B	0.6 GB	733	62.3
GPT-2 124M	0.14B	0.6 GB	733	6.5
SmolLM2 135M	0.135B	0.6 GB	760	7.0
Falcon-H1R Tiny 90M	0.09B	0.5 GB	1140	—