FitMyLLM
▸ NVIDIA· PASCAL

NVIDIA Jetson TX2

Running LLMs on the Jetson TX2 — 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
60
GB/S
MODELS Q4
201/449
45%
7B Q4 SPEED
~7
SLOW
▸ 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
~16
TOK/S
7B
~7
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
60 GB/s
FP16 COMPUTE
1.3 TFLOPS
TDP
15W
MEMORY
LPDDR4
ARCHITECTURE
Pascal
CUDA CORES
256
PCIE
Gen 2 x4
50
FAST MODELS · >30 TOK/S
Real-time chat speed
108
USABLE · >10 TOK/S
Comfortable for all tasks
201
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Jetson TX2 (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
533
TOK/S · 7% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
356
TOK/S · 7% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
343
TOK/S · 7% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
343
TOK/S · 7% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
188
TOK/S · 8% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
178
TOK/S · 8% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
157
TOK/S · 8% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
143
TOK/S · 9% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
143
TOK/S · 9% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
143
TOK/S · 9% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
137
TOK/S · 9% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
133
TOK/S · 9% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
126
TOK/S · 9% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
96
TOK/S · 10% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
96
TOK/S · 10% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
92
TOK/S · 10% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
85
TOK/S · 10% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
85
TOK/S · 10% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
80
TOK/S · 11% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
80
TOK/S · 11% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
80
TOK/S · 11% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
77
TOK/S · 11% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
65
TOK/S · 12% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
64
TOK/S · 12% VRAM
›
A
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
59
TOK/S · 12% VRAM
›
A
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
55
TOK/S · 13% VRAM
›
A
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
51
TOK/S · 13% VRAM
›
A
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
44
TOK/S · 15% VRAM
›
A
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
44
TOK/S · 14% VRAM
›
A
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
40
TOK/S · 15% VRAM
›
A
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
40
TOK/S · 15% VRAM
›
A
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
40
TOK/S · 15% VRAM
›
A
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
40
TOK/S · 15% VRAM
›
B
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
37
TOK/S · 16% VRAM
›
B
OPT 1.3B1.3B
OPT·2K CTX· CHAT
37
TOK/S · 16% VRAM
›
B
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
37
TOK/S · 16% VRAM
›
B
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
36
TOK/S · 16% VRAM
›
B
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
34
TOK/S · 17% VRAM
›
B
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
34
TOK/S · 17% VRAM
›
B
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
32
TOK/S · 70% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Jetson TX2.

WHAT THIS CARD IS WORTH

Jetson TX2 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 GB8 tok/sEST
Bamba 9B v29.78B · Q4_K_M7.2 GB8 tok/sEST
RecurrentGemma 9B9.63B · Q4_K_M6.8 GB8 tok/sEST
glm-4-9b9.4B · Q4_K_M6.6 GB8 tok/sEST
Yi 1.5 9B9B · Q4_K_M6.5 GB8 tok/sEST
Yi Coder 9B9B · Q4_K_M6.5 GB8 tok/sEST
Ministral 3 8B8.92B · Q4_K_M6.6 GB8 tok/sEST
Ministral 3 8B Reasoning8.92B · Q4_K_M6.6 GB8 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 TX28 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 TX2 — 8 GB VRAM.

▸ JETSON TX2 SPEC
BRAND
NVIDIA
VRAM
8 GB LPDDR4
BANDWIDTH
60 GB/s
FP16 COMPUTE
1.3 TFLOPS
FP32 COMPUTE
0.7 TFLOPS
CUDA CORES
256
TDP
15 W
ARCHITECTURE
Pascal
▸ AI CAPABILITY
201/ 449 models @ Q4

With 8 GB VRAM and 60 GB/s bandwidth, this GPU handles models up to 9.63B parameters.

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

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR JETSON TX2
201 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
RecurrentGemma 9B9.63B6.4 GB535.0
glm-4-9b9.4B6.2 GB520.5
gemma-2-9b9.2B6.1 GB530.2
Yi 1.5 9B9B6.0 GB530.3
Yi Coder 9B9B6.0 GB535.8
Ministral 3 8B8.92B5.9 GB525.7
Ministral 3 8B Reasoning8.92B5.9 GB5—
NVIDIA-Nemotron-Nano-9B-v28.9B5.9 GB544.2
InternLM3 8B Instruct8.8B5.9 GB538.7
Gemma 1 7B8.54B5.7 GB624.7
CodeGemma 7B8.54B5.7 GB640.2
LFM2 8B A1B8.3B5.6 GB3224.3
Seed-Coder 8B Instruct8.25B5.5 GB634.1
Seed-Coder 8B Reasoning8.25B5.5 GB632.9
DeepSeek R1-0528 Qwen3 8B8.2B5.5 GB636.3
Qwen3-8B8.2B5.5 GB643.3
Granite 3.0 8B8.17B5.5 GB636.4
Granite 3.1 8B8.17B5.5 GB638.6
Command-R7B8.03B5.4 GB635.3
Aya Expanse 8B8B5.4 GB627.8