FitMyLLM
▸ NVIDIA· MAXWELL 2.0

NVIDIA GRID M60-2Q

Running LLMs on the GRID M60-2Q — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
160.4
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~18
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
160.4 GB/s
FP16 COMPUTE
4.8 TFLOPS
TDP
225W
MEMORY
GDDR5
ARCHITECTURE
Maxwell 2.0
CUDA CORES
2,048
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
▸ RENT IT IN THE CLOUD

Buying GRID M60-2Q costs $15–$40k and isn’t practical for most teams. Spin one up by the hour instead:

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
1426
TOK/S · 27% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
951
TOK/S · 29% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
917
TOK/S · 29% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
917
TOK/S · 29% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
501
TOK/S · 32% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
475
TOK/S · 33% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
421
TOK/S · 34% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
383
TOK/S · 35% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
383
TOK/S · 35% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
383
TOK/S · 35% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
367
TOK/S · 35% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
356
TOK/S · 35% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
338
TOK/S · 36% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
257
TOK/S · 40% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
257
TOK/S · 40% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
247
TOK/S · 40% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
226
TOK/S · 42% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
226
TOK/S · 42% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
214
TOK/S · 43% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
214
TOK/S · 43% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
214
TOK/S · 43% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
207
TOK/S · 43% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
173
TOK/S · 47% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
171
TOK/S · 47% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
158
TOK/S · 49% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
147
TOK/S · 51% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
137
TOK/S · 53% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
119
TOK/S · 57% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
117
TOK/S · 58% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
107
TOK/S · 61% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
107
TOK/S · 61% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
107
TOK/S · 61% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
107
TOK/S · 61% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
99
TOK/S · 64% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
99
TOK/S · 64% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
99
TOK/S · 64% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
95
TOK/S · 66% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
90
TOK/S · 68% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
90
TOK/S · 68% VRAM
›
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
86
TOK/S · 70% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your GRID M60-2Q.

WHAT THIS CARD IS WORTH

GRID M60-2Q 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 GB88 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB86 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB90 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB81 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB83 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB83 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB83 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB83 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
GRID M60-2Q2 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 GRID M60-2Q — 2 GB VRAM.

▸ GRID M60-2Q SPEC
BRAND
NVIDIA
VRAM
2 GB GDDR5
BANDWIDTH
160.4 GB/s
FP16 COMPUTE
4.8 TFLOPS
FP32 COMPUTE
4.8 TFLOPS
CUDA CORES
2,048
TDP
225 W
ARCHITECTURE
Maxwell 2.0
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 160.4 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~18 tok/s.

§ 01TOP MODELS FOR GRID M60-2Q
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB805.1
stablelm-2-1_6b1.6B1.5 GB809.5
Falcon-H1 1.5B1.55B1.4 GB8343.8
Qwen2.5-Coder-1.5B1.5B1.4 GB8619.6
Qwen2 Math 1.5B1.5B1.4 GB8619.6
Qwen 2.5 1.5B1.5B1.4 GB8630.2
Yi Coder 1.5B1.5B1.4 GB8614.6
Stella en 1.5B v51.5B1.4 GB86—
Phi-1 1.3B1.42B1.4 GB907.2
Phi-1.5 1.3B1.42B1.4 GB907.2
DeepSeek Coder 1.3B1.35B1.3 GB9516.8
EXAONE-4.0-1.2B1.3B1.3 GB9918.9
OPT 1.3B1.3B1.3 GB995.3
MiniCPM-V 4.61.3B1.3 GB9921.5
LFM2.5-1.2B-Thinking1.2B1.2 GB10719.6
Llama-3.2-1B1.2B1.2 GB10710.1
LFM2 1.2B1.2B1.2 GB10715.6
Zamba2 1.2B1.2B1.2 GB10741.5
TinyLlama 1.1B1.1B1.2 GB11713.6
MiniCPM5 1B1.08B1.1 GB11926.5