FitMyLLM
▸ NVIDIA· MAXWELL 2.0

NVIDIA Jetson TX1

Running LLMs on the Jetson TX1 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
4 GB
BUDGET
BANDWIDTH
10
GB/S
MODELS Q4
97/449
22%
7B Q4 SPEED
~1
SLOW
▸ 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
~3
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
10 GB/s
FP16 COMPUTE
1 TFLOPS
TDP
6W
MEMORY
LPDDR4
ARCHITECTURE
Maxwell 2.0
CUDA CORES
256
PCIE
Gen 2 x4
6
FAST MODELS · >30 TOK/S
Real-time chat speed
25
USABLE · >10 TOK/S
Comfortable for all tasks
97
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Jetson TX1 (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 97
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
89
TOK/S · 14% VRAM
›
A
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
59
TOK/S · 14% VRAM
›
A
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
57
TOK/S · 14% VRAM
›
A
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
57
TOK/S · 14% VRAM
›
B
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
31
TOK/S · 16% VRAM
›
B
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
30
TOK/S · 16% VRAM
›
B
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
26
TOK/S · 17% VRAM
›
C
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
24
TOK/S · 17% VRAM
›
C
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
24
TOK/S · 17% VRAM
›
C
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
24
TOK/S · 17% VRAM
›
C
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
23
TOK/S · 18% VRAM
›
C
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
22
TOK/S · 18% VRAM
›
C
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
21
TOK/S · 18% VRAM
›
C
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
16
TOK/S · 20% VRAM
›
C
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
16
TOK/S · 20% VRAM
›
C
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
15
TOK/S · 20% VRAM
›
D
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
14
TOK/S · 21% VRAM
›
D
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
14
TOK/S · 21% VRAM
›
D
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
13
TOK/S · 22% VRAM
›
D
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
13
TOK/S · 21% VRAM
›
D
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
13
TOK/S · 21% VRAM
›
D
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
13
TOK/S · 21% VRAM
›
D
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
11
TOK/S · 24% VRAM
›
D
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
11
TOK/S · 24% VRAM
›
D
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
10
TOK/S · 25% VRAM
›
D
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
9
TOK/S · 27% VRAM
›
D
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
9
TOK/S · 26% VRAM
›
F
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
7
TOK/S · 31% VRAM
›
F
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
7
TOK/S · 31% VRAM
›
F
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
7
TOK/S · 31% VRAM
›
F
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
7
TOK/S · 31% VRAM
›
F
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
7
TOK/S · 29% VRAM
›
F
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
7
TOK/S · 29% VRAM
›
F
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
6
TOK/S · 34% VRAM
›
F
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
6
TOK/S · 34% VRAM
›
F
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
6
TOK/S · 33% VRAM
›
F
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
6
TOK/S · 32% VRAM
›
F
OPT 1.3B1.3B
OPT·2K CTX· CHAT
6
TOK/S · 32% VRAM
›
F
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
6
TOK/S · 32% VRAM
›
F
SmolLM2 1.7B1.71B
SMOLLM·8K CTX· CHAT
5
TOK/S · 38% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Jetson TX1.

WHAT THIS CARD IS WORTH

Jetson TX1 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 GB3 tok/sEST
Ministral 3 3B Reasoning4.25B · Q4_K_M3.3 GB3 tok/sEST
Qwen3 4B4B · Q4_K_M3.3 GB3 tok/sEST
Qwen3-4B Instruct 25074B · Q4_K_M3.3 GB3 tok/sEST
Qwen3-Embedding 4B4B · Q4_K_M3.3 GB3 tok/sEST
Nemotron 3 Nano 4B3.97B · Q4_K_M3.4 GB3 tok/sEST
Ministral 3 3B3.85B · Q5_K_M3.5 GB3 tok/sEST
phi-3-mini-4k 3.8B3.8B · Q4_K_M3.4 GB3 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
Jetson TX14 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 TX1 — 4 GB VRAM.

▸ JETSON TX1 SPEC
BRAND
NVIDIA
VRAM
4 GB LPDDR4
BANDWIDTH
10 GB/s
FP16 COMPUTE
1 TFLOPS
FP32 COMPUTE
0.5 TFLOPS
CUDA CORES
256
TDP
6 W
ARCHITECTURE
Maxwell 2.0
▸ AI CAPABILITY
97/ 449 models @ Q4

With 4 GB VRAM and 10 GB/s bandwidth, this GPU handles models up to 4.25B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~1 tok/s.

§ 01TOP MODELS FOR JETSON TX1
97 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Ministral 3 3B Reasoning4.25B3.1 GB2—
Qwen 1.5 4B4B2.9 GB212.6
Qwen3 4B4B2.9 GB240.7
Qwen3-4B Instruct 25074B2.9 GB237.2
Qwen3-Embedding 4B4B2.9 GB2—
Nemotron 3 Nano 4B3.97B2.9 GB232.0
Ministral 3 3B3.85B2.8 GB221.4
Phi-3.5 Mini 3.8B3.82B2.8 GB246.6
phi-3-mini-4k 3.8B3.8B2.8 GB230.5
Phi-4-mini 3.8B3.8B2.8 GB249.0
Cogito 3B3.61B2.7 GB222.1
Falcon3-3B3.23B2.5 GB225.7
granite-4.0-h-micro 3.2B3.2B2.4 GB318.4
Llama-3.2-3B3.2B2.4 GB317.9
Falcon-H1 3B3.15B2.4 GB349.5
Qwen 2.5 3B3.1B2.4 GB337.2
SmolLM3-3B3.1B2.4 GB330.5
Ministral 3B3B2.3 GB329.6
StarCoder2 3B3B2.3 GB39.5
Granite 4.1 3B3B2.3 GB316.6