FitMyLLM
NVIDIA· AMPERE

NVIDIA RTX A1000 Mobile

Running LLMs on the RTX A1000 Mobile — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
4 GB
BUDGET
BANDWIDTH
176
GB/S
MODELS Q4
97/449
22%
7B Q4 SPEED
~20
GOOD
▸ MODEL COVERAGE @ Q422% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~47
TOK/S
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
4 GB
BANDWIDTH
176 GB/s
FP16 COMPUTE
4.7 TFLOPS
TDP
60W
MEMORY
GDDR6
ARCHITECTURE
Ampere
CUDA CORES
2,048
TENSOR CORES
64
PCIE
Gen 4 x8
97
FAST MODELS · >30 TOK/S
Real-time chat speed
97
USABLE · >10 TOK/S
Comfortable for all tasks
97
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying RTX A1000 Mobile 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· 97
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
1564
TOK/S · 14% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1043
TOK/S · 14% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1006
TOK/S · 14% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1006
TOK/S · 14% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
550
TOK/S · 16% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
521
TOK/S · 16% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
462
TOK/S · 17% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
420
TOK/S · 17% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
420
TOK/S · 17% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
420
TOK/S · 17% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
402
TOK/S · 18% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
391
TOK/S · 18% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
371
TOK/S · 18% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
282
TOK/S · 20% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
282
TOK/S · 20% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
271
TOK/S · 20% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
248
TOK/S · 21% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
248
TOK/S · 21% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
235
TOK/S · 21% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
235
TOK/S · 21% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
235
TOK/S · 21% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
227
TOK/S · 22% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
190
TOK/S · 24% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
188
TOK/S · 24% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
174
TOK/S · 25% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
162
TOK/S · 26% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
150
TOK/S · 27% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
130
TOK/S · 29% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
128
TOK/S · 29% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
117
TOK/S · 31% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
117
TOK/S · 31% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
117
TOK/S · 31% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
117
TOK/S · 31% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
108
TOK/S · 32% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
108
TOK/S · 32% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
108
TOK/S · 32% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
104
TOK/S · 33% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
99
TOK/S · 34% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
99
TOK/S · 34% VRAM
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
94
TOK/S · 35% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your RTX A1000 Mobile.

WHAT THIS CARD IS WORTH

RTX A1000 Mobile holds 97 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is InternLM2 5B 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
InternLM2 5B4.5B · Q4_K_M3.6 GB40 tok/sEST
Ministral 3 3B Reasoning4.25B · Q4_K_M3.3 GB42 tok/sEST
Qwen3 4B4B · Q4_K_M3.3 GB45 tok/sEST
Qwen3-4B Instruct 25074B · Q4_K_M3.3 GB45 tok/sEST
Qwen3-Embedding 4B4B · Q4_K_M3.3 GB45 tok/sEST
Nemotron 3 Nano 4B3.97B · Q4_K_M3.4 GB45 tok/sEST
Ministral 3 3B3.85B · Q5_K_M3.5 GB40 tok/sEST
phi-3-mini-4k 3.8B3.8B · Q4_K_M3.4 GB47 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
RTX 3070 Ti8 GB$49912.6 tok/s per $100
RTX 3060 Ti GDDR6X8 GB$39915.8 tok/s per $100
RTX 3070 Ti 8 GB GA1028 GB$59910.5 tok/s per $100
Arc A7508 GB$19910.1 tok/s per $100
RTX A1000 Mobile4 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 RTX A1000 Mobile — 4 GB VRAM.

RTX A1000 MOBILE SPEC
BRAND
NVIDIA
VRAM
4 GB GDDR6
BANDWIDTH
176 GB/s
FP16 COMPUTE
4.7 TFLOPS
FP32 COMPUTE
4.7 TFLOPS
CUDA CORES
2,048
TENSOR CORES
64
TDP
60 W
ARCHITECTURE
Ampere
▸ AI CAPABILITY
97/ 449 models @ Q4

With 4 GB VRAM and 176 GB/s bandwidth, this GPU handles models up to 4.25B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~20 tok/s.

§ 01TOP MODELS FOR RTX A1000 MOBILE
97 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Ministral 3 3B Reasoning4.25B3.1 GB33
Qwen 1.5 4B4B2.9 GB3512.6
Qwen3 4B4B2.9 GB3540.7
Qwen3-4B Instruct 25074B2.9 GB3537.2
Qwen3-Embedding 4B4B2.9 GB35
Nemotron 3 Nano 4B3.97B2.9 GB3532.0
Ministral 3 3B3.85B2.8 GB3721.4
Phi-3.5 Mini 3.8B3.82B2.8 GB3746.6
phi-3-mini-4k 3.8B3.8B2.8 GB3730.5
Phi-4-mini 3.8B3.8B2.8 GB3749.0
Cogito 3B3.61B2.7 GB3922.1
Falcon3-3B3.23B2.5 GB4425.7
granite-4.0-h-micro 3.2B3.2B2.4 GB4418.4
Llama-3.2-3B3.2B2.4 GB4417.9
Falcon-H1 3B3.15B2.4 GB4549.5
Qwen 2.5 3B3.1B2.4 GB4537.2
SmolLM3-3B3.1B2.4 GB4530.5
Ministral 3B3B2.3 GB4729.6
StarCoder2 3B3B2.3 GB479.5
Granite 4.1 3B3B2.3 GB4716.6