FitMyLLM
NVIDIA· AMPERE

NVIDIA Jetson Orin Nano Super

Running LLMs on the Jetson Orin Nano Super — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
8 GB
ENTRY-LEVEL
BANDWIDTH
102
GB/S
MODELS Q4
201/449
45%
7B Q4 SPEED
~12
USABLE
▸ MODEL COVERAGE @ Q445% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~27
TOK/S
7B
~12
TOK/S
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
8 GB
BANDWIDTH
102 GB/s
FP16 COMPUTE
4.2 TFLOPS
TDP
25W
MEMORY
LPDDR5
ARCHITECTURE
Ampere
CUDA CORES
1,024
TENSOR CORES
32
PCIE
Gen 4 x4
73
FAST MODELS · >30 TOK/S
Real-time chat speed
192
USABLE · >10 TOK/S
Comfortable for all tasks
201
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Jetson Orin Nano Super (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 201
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
907
TOK/S · 7% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
604
TOK/S · 7% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
583
TOK/S · 7% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
583
TOK/S · 7% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
319
TOK/S · 8% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
302
TOK/S · 8% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
268
TOK/S · 8% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
244
TOK/S · 9% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
244
TOK/S · 9% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
244
TOK/S · 9% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
233
TOK/S · 9% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
227
TOK/S · 9% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
215
TOK/S · 9% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
163
TOK/S · 10% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
163
TOK/S · 10% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
157
TOK/S · 10% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
144
TOK/S · 10% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
144
TOK/S · 10% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
136
TOK/S · 11% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
136
TOK/S · 11% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
136
TOK/S · 11% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
132
TOK/S · 11% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
110
TOK/S · 12% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
109
TOK/S · 12% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
101
TOK/S · 12% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
94
TOK/S · 13% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
87
TOK/S · 13% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
76
TOK/S · 14% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
74
TOK/S · 15% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
68
TOK/S · 15% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
68
TOK/S · 15% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
68
TOK/S · 15% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
68
TOK/S · 15% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
63
TOK/S · 16% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
63
TOK/S · 16% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
63
TOK/S · 16% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
60
TOK/S · 16% VRAM
A
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
57
TOK/S · 17% VRAM
A
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
57
TOK/S · 17% VRAM
A
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
54
TOK/S · 70% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Jetson Orin Nano Super.

WHAT THIS CARD IS WORTH

Jetson Orin Nano Super holds 201 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Falcon3-10B at IQ4_XS.

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
Falcon3-10B10.3B · IQ4_XS7.1 GB13 tok/sEST
Bamba 9B v29.78B · Q4_K_M7.2 GB12 tok/sEST
RecurrentGemma 9B9.63B · Q4_K_M6.8 GB13 tok/sEST
glm-4-9b9.4B · Q4_K_M6.6 GB13 tok/sEST
Yi 1.5 9B9B · Q4_K_M6.5 GB13 tok/sEST
Yi Coder 9B9B · Q4_K_M6.5 GB13 tok/sEST
Ministral 3 8B8.92B · Q4_K_M6.6 GB14 tok/sEST
Ministral 3 8B Reasoning8.92B · Q4_K_M6.6 GB14 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M3 Pro (18GB)12 GB$1,5991.5 tok/s per $100
M1 Pro (16GB)11 GB$9992.9 tok/s per $100
M2 Pro (16GB)11 GB$1,2992.2 tok/s per $100
M4 (16GB)11 GB$4994.0 tok/s per $100
Jetson Orin Nano Super8 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 Jetson Orin Nano Super — 8 GB VRAM.

JETSON ORIN NANO SUPER SPEC
BRAND
NVIDIA
VRAM
8 GB LPDDR5
BANDWIDTH
102 GB/s
FP16 COMPUTE
4.2 TFLOPS
FP32 COMPUTE
2.1 TFLOPS
CUDA CORES
1,024
TENSOR CORES
32
TDP
25 W
ARCHITECTURE
Ampere
▸ AI CAPABILITY
201/ 449 models @ Q4

With 8 GB VRAM and 102 GB/s bandwidth, this GPU handles models up to 9.63B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~12 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR JETSON ORIN NANO SUPER
201 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
RecurrentGemma 9B9.63B6.4 GB835.0
glm-4-9b9.4B6.2 GB920.5
gemma-2-9b9.2B6.1 GB930.2
Yi 1.5 9B9B6.0 GB930.3
Yi Coder 9B9B6.0 GB935.8
Ministral 3 8B8.92B5.9 GB925.7
Ministral 3 8B Reasoning8.92B5.9 GB9
NVIDIA-Nemotron-Nano-9B-v28.9B5.9 GB944.2
InternLM3 8B Instruct8.8B5.9 GB938.7
Gemma 1 7B8.54B5.7 GB1024.7
CodeGemma 7B8.54B5.7 GB1040.2
LFM2 8B A1B8.3B5.6 GB5424.3
Seed-Coder 8B Instruct8.25B5.5 GB1034.1
Seed-Coder 8B Reasoning8.25B5.5 GB1032.9
DeepSeek R1-0528 Qwen3 8B8.2B5.5 GB1036.3
Qwen3-8B8.2B5.5 GB1043.3
Granite 3.0 8B8.17B5.5 GB1036.4
Granite 3.1 8B8.17B5.5 GB1038.6
Command-R7B8.03B5.4 GB1035.3
Aya Expanse 8B8B5.4 GB1027.8