FitMyLLM
NVIDIA· TESLA 2.0

NVIDIA GeForce GTX 285 X2

Running LLMs on the GeForce GTX 285 X2 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
148
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~17
GOOD
▸ 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
148 GB/s
FP16 COMPUTE
0.7 TFLOPS
TDP
315W
MEMORY
GDDR3
ARCHITECTURE
Tesla 2.0
CUDA CORES
240
PCIE
Gen 2 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 285 X2 (or anything bigger) without committing. Pay by the second, cancel anytime.

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

Some links are affiliate links — we may earn a small commission at no extra cost to you. This helps keep FitMyLLM free and independent.

▸ COMPATIBLE MODELS· 47
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
1316
TOK/S · 27% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
877
TOK/S · 29% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
846
TOK/S · 29% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
846
TOK/S · 29% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
462
TOK/S · 32% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
439
TOK/S · 33% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
388
TOK/S · 34% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
353
TOK/S · 35% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
353
TOK/S · 35% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
353
TOK/S · 35% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
338
TOK/S · 35% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
329
TOK/S · 35% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
312
TOK/S · 36% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
237
TOK/S · 40% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
237
TOK/S · 40% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
228
TOK/S · 40% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
208
TOK/S · 42% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
208
TOK/S · 42% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
197
TOK/S · 43% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
197
TOK/S · 43% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
197
TOK/S · 43% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
191
TOK/S · 43% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
160
TOK/S · 47% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
158
TOK/S · 47% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
146
TOK/S · 49% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
136
TOK/S · 51% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
126
TOK/S · 53% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
110
TOK/S · 57% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
108
TOK/S · 58% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
99
TOK/S · 61% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
99
TOK/S · 61% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
99
TOK/S · 61% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
99
TOK/S · 61% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
91
TOK/S · 64% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
91
TOK/S · 64% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
91
TOK/S · 64% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
88
TOK/S · 66% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
83
TOK/S · 68% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
83
TOK/S · 68% VRAM
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
79
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 285 X2.

WHAT THIS CARD IS WORTH

GTX 285 X2 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 GB83 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB81 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB85 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB76 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB79 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB79 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB79 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB79 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 285 X22 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 285 X2 — 2 GB VRAM.

GEFORCE GTX 285 X2 SPEC
BRAND
NVIDIA
VRAM
2 GB GDDR3
BANDWIDTH
148 GB/s
FP16 COMPUTE
0.7 TFLOPS
FP32 COMPUTE
0.7 TFLOPS
CUDA CORES
240
TDP
315 W
ARCHITECTURE
Tesla 2.0
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 148 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~17 tok/s.

§ 01TOP MODELS FOR GEFORCE GTX 285 X2
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB745.1
stablelm-2-1_6b1.6B1.5 GB749.5
Falcon-H1 1.5B1.55B1.4 GB7643.8
Qwen2.5-Coder-1.5B1.5B1.4 GB7919.6
Qwen2 Math 1.5B1.5B1.4 GB7919.6
Qwen 2.5 1.5B1.5B1.4 GB7930.2
Yi Coder 1.5B1.5B1.4 GB7914.6
Stella en 1.5B v51.5B1.4 GB79
Phi-1 1.3B1.42B1.4 GB837.2
Phi-1.5 1.3B1.42B1.4 GB837.2
DeepSeek Coder 1.3B1.35B1.3 GB8816.8
EXAONE-4.0-1.2B1.3B1.3 GB9118.9
OPT 1.3B1.3B1.3 GB915.3
MiniCPM-V 4.61.3B1.3 GB9121.5
LFM2.5-1.2B-Thinking1.2B1.2 GB9919.6
Llama-3.2-1B1.2B1.2 GB9910.1
LFM2 1.2B1.2B1.2 GB9915.6
Zamba2 1.2B1.2B1.2 GB9941.5
TinyLlama 1.1B1.1B1.2 GB10813.6
MiniCPM5 1B1.08B1.1 GB11026.5