FitMyLLM
ARM· IMMORTALIS GEN4

Google Tensor G4 GPU

Running LLMs on the Google Tensor G4 GPU — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
12 GB
ENTRY-LEVEL
BANDWIDTH
51.2
GB/S
MODELS Q4
248/449
55%
7B Q4 SPEED
~7
SLOW
▸ MODEL COVERAGE @ Q455% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~15
TOK/S
7B
~7
TOK/S
14B
~3
TOK/S
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
12 GB
BANDWIDTH
51.2 GB/s
FP16 COMPUTE
2 TFLOPS
TDP
8W
MEMORY
Shared
ARCHITECTURE
Immortalis Gen4
46
FAST MODELS · >30 TOK/S
Real-time chat speed
107
USABLE · >10 TOK/S
Comfortable for all tasks
248
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Google Tensor G4 GPU (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 248
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
506
TOK/S · 5% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
337
TOK/S · 5% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
325
TOK/S · 5% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
325
TOK/S · 5% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
178
TOK/S · 5% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
169
TOK/S · 5% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
149
TOK/S · 6% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
136
TOK/S · 6% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
136
TOK/S · 6% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
136
TOK/S · 6% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
130
TOK/S · 6% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
126
TOK/S · 6% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
120
TOK/S · 6% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
91
TOK/S · 7% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
91
TOK/S · 7% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
88
TOK/S · 7% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
80
TOK/S · 7% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
80
TOK/S · 7% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
76
TOK/S · 7% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
76
TOK/S · 7% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
76
TOK/S · 7% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
73
TOK/S · 7% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
62
TOK/S · 8% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
61
TOK/S · 8% VRAM
A
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
56
TOK/S · 8% VRAM
A
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
52
TOK/S · 9% VRAM
A
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
48
TOK/S · 9% VRAM
A
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
42
TOK/S · 10% VRAM
A
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
41
TOK/S · 10% VRAM
B
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
38
TOK/S · 10% VRAM
B
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
38
TOK/S · 10% VRAM
B
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
38
TOK/S · 10% VRAM
B
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
38
TOK/S · 10% VRAM
B
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
35
TOK/S · 11% VRAM
B
OPT 1.3B1.3B
OPT·2K CTX· CHAT
35
TOK/S · 11% VRAM
B
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
35
TOK/S · 11% VRAM
B
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
34
TOK/S · 11% VRAM
B
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
32
TOK/S · 11% VRAM
B
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
32
TOK/S · 11% VRAM
B
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
30
TOK/S · 46% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Google Tensor G4 GPU.

WHAT THIS CARD IS WORTH

Google Tensor G4 GPU holds 248 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Ling-lite 16.8B 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
Ling-lite 16.8B16.8B · IQ4_XS10.7 GB29 tok/sEST
DeepSeek V2 Lite 16B16B · Q4_K_S10.6 GB27 tok/sEST
StarCoder2 15B15.96B · Q4_K_S10.7 GB4 tok/sEST
DeepSeek-Coder-V2-Lite 15.7B15.7B · Q4_K_S10.4 GB27 tok/sEST
DeepSeek R1 Distill Qwen 14B14.8B · Q4_K_S10.4 GB4 tok/sEST
DeepCoder 14B14.8B · Q4_K_S10.4 GB4 tok/sEST
Qwen2.5-Coder-14B14.8B · Q4_K_S10.4 GB4 tok/sEST
Qwen2.5-14B14.8B · Q4_K_S10.4 GB4 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M4 Pro (24GB)16 GB$1,3992.5 tok/s per $100
M4 (24GB)16 GB$6992.9 tok/s per $100
M2 (24GB)16 GB$9991.7 tok/s per $100
M3 (24GB)16 GB$9991.7 tok/s per $100
Google Tensor G4 GPU12 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

Google Tensor G4 GPU — 12 GB VRAM.

GOOGLE TENSOR G4 GPU SPEC
BRAND
Apple
VRAM
12 GB Shared
BANDWIDTH
51.2 GB/s
FP16 COMPUTE
2 TFLOPS
TDP
8 W
ARCHITECTURE
Immortalis Gen4
▸ AI CAPABILITY
248/ 449 models @ Q4

With 12 GB VRAM and 51.2 GB/s bandwidth, this GPU handles models up to 14.8B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~6 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR GOOGLE TENSOR G4 GPU
248 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
DeepSeek R1 Distill Qwen 14B14.8B9.5 GB343.9
DeepCoder 14B14.8B9.5 GB338.7
Qwen2.5-Coder-14B14.8B9.5 GB341.3
Qwen2.5-14B14.8B9.5 GB341.3
Qwen3 14B14.8B9.5 GB345.7
phi-4 14B14.66B9.4 GB333.7
Phi-4-reasoning 14B14.66B9.4 GB333.7
Phi-4-reasoning-plus 14B14.66B9.4 GB375.5
Phi-3-medium-14b14B9.0 GB333.7
Qwen 1.5 14B14B9.0 GB341.3
Ministral 3 14B Reasoning13.95B9.0 GB3
Baichuan2 13B13B8.4 GB423.6
Llama 2 13B13B8.4 GB417.2
CodeLlama 13B13B8.4 GB419.7
Vicuna 13B13B8.4 GB411.8
LLaMA 1 13B13B8.4 GB432.9
OPT 13B13B8.4 GB435.8
Orca 2 13B13B8.4 GB425.4
WizardCoder Python 13B13B8.4 GB460.1
WizardLM 13B13B8.4 GB419.5