FitMyLLM
INTEL· GENERATION 12.5

Intel Data Center GPU Max Subsystem

Running LLMs on the Data Center GPU Max Subsystem — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
128 GB
FLAGSHIP
BANDWIDTH
3210
GB/S
MODELS Q4
397/449
88%
7B Q4 SPEED
~147
BLAZING
▸ MODEL COVERAGE @ Q488% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~342
TOK/S
7B
~147
TOK/S
14B
~73
TOK/S
32B
~32
TOK/S
70B
~15
TOK/S
▸ 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
128 GB
BANDWIDTH
3210 GB/s
FP16 COMPUTE
52.4 TFLOPS
TDP
2400W
MEMORY
HBM2e
ARCHITECTURE
Generation 12.5
COMPUTE UNITS
1024
PCIE
Gen 5 x16
351
FAST MODELS · >30 TOK/S
Real-time chat speed
388
USABLE · >10 TOK/S
Comfortable for all tasks
397
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying Intel Data Center GPU Max Subsystem 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· 397
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
11413
TOK/S · 0% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
7609
TOK/S · 0% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
7337
TOK/S · 0% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
7337
TOK/S · 0% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
4012
TOK/S · 1% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
3804
TOK/S · 1% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
3368
TOK/S · 1% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
3066
TOK/S · 1% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
3066
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
3066
TOK/S · 1% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
2935
TOK/S · 1% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
2853
TOK/S · 1% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
2703
TOK/S · 1% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
2054
TOK/S · 1% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
2054
TOK/S · 1% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
1975
TOK/S · 1% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
1808
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1808
TOK/S · 1% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
1712
TOK/S · 1% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
1712
TOK/S · 1% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
1712
TOK/S · 1% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
1657
TOK/S · 1% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
1388
TOK/S · 1% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
1370
TOK/S · 1% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
1268
TOK/S · 1% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
1181
TOK/S · 1% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
1093
TOK/S · 1% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
951
TOK/S · 1% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
934
TOK/S · 1% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
856
TOK/S · 1% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
856
TOK/S · 1% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
856
TOK/S · 1% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
856
TOK/S · 1% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
790
TOK/S · 1% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
790
TOK/S · 1% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
790
TOK/S · 1% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
761
TOK/S · 1% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
723
TOK/S · 1% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
723
TOK/S · 1% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
685
TOK/S · 4% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Data Center GPU Max Subsystem.

WHAT THIS CARD IS WORTH

Data Center GPU Max Subsystem holds 397 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Step 3.7 Flash 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
Step 3.7 Flash201.37B · IQ4_XS115.0 GB94 tok/sEST
Falcon 180B180B · Q4_K_M113.8 GB5 tok/sEST
dots.llm1.inst 142.8B142.8B · Q5_K_M108.1 GB6 tok/sEST
WizardLM 2 8x22B141B · Q5_K_M103.8 GB21 tok/sEST
Mixtral-8x22B140.6B · Q5_K_M103.6 GB21 tok/sEST
DBRX 132B132B · Q6_K111.4 GB20 tok/sEST
Mistral Medium 3.5128B · Q6_K108.8 GB6 tok/sEST
Pixtral Large 124B124B · Q6_K105.6 GB6 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M4 Ultra (192GB)144 GB$7,4991.2 tok/s per $100
M2 Ultra (192GB)144 GB$5,4991.3 tok/s per $100
M3 Ultra (192GB)144 GB$6,9991.0 tok/s per $100
B300144 GB$35,0000.1 tok/s per $100
Data Center GPU Max Subsystem128 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

Intel Data Center GPU Max Subsystem — 128 GB VRAM.

DATA CENTER GPU MAX SUBSYSTEM SPEC
BRAND
Intel
VRAM
128 GB HBM2e
BANDWIDTH
3210 GB/s
FP16 COMPUTE
52.4 TFLOPS
FP32 COMPUTE
52.4 TFLOPS
TDP
2400 W
ARCHITECTURE
Generation 12.5
▸ AI CAPABILITY
397/ 449 models @ Q4

With 128 GB VRAM and 3210 GB/s bandwidth, this GPU handles models up to 180B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~367 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR DATA CENTER GPU MAX SUBSYSTEM
397 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Falcon 180B180B110.5 GB651.3
bloom 176.2B176.2B108.2 GB615.0
dots.llm1.inst 142.8B142.8B87.8 GB7
WizardLM 2 8x22B141B86.7 GB2642.4
Mixtral-8x22B140.6B86.4 GB2631.9
DBRX 132B132B81.2 GB2946.3
Mistral Medium 3.5128B78.7 GB846.6
Pixtral Large 124B124B76.3 GB839.3
Nemotron 3 Super 120B-A12B123.61B76.0 GB8653.2
Mistral-Large 123B123B75.7 GB833.5
Devstral 2 123B123B75.7 GB838.1
Qwen 3.5 122B A10B122B75.1 GB10356.8
Nemotron 3 Super 120B120B73.8 GB8657.3
Mistral Small 4 119B119B73.2 GB15850.2
GPT-OSS 120B117B72.0 GB20154.1
Command A 111B111B68.3 GB927.6
GLM 4.5 Air110B67.7 GB8651.0
Qwen 1.5 110B110B67.7 GB933.4
Llama 4 Scout 17B-16E109B67.1 GB6033.9
Cogito v2 109B MoE109B67.1 GB60