FitMyLLM
▸ INTEL· GENERATION 12.5

Intel Data Center GPU Max 1550

Running LLMs on the Data Center GPU Max 1550 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
128 GB
FLAGSHIP
BANDWIDTH
3280
GB/S
MODELS Q4
397/449
88%
7B Q4 SPEED
~150
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
~350
TOK/S
7B
~150
TOK/S
14B
~75
TOK/S
32B
~33
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
3280 GB/s
FP16 COMPUTE
52.4 TFLOPS
TDP
600W
MEMORY
HBM2e
ARCHITECTURE
Generation 12.5
COMPUTE UNITS
1024
PCIE
Gen 5 x16
352
FAST MODELS · >30 TOK/S
Real-time chat speed
389
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 1550 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
11662
TOK/S · 0% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
7775
TOK/S · 0% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
7497
TOK/S · 0% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
7497
TOK/S · 0% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
4100
TOK/S · 1% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
3887
TOK/S · 1% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
3441
TOK/S · 1% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
3133
TOK/S · 1% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
3133
TOK/S · 1% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
3133
TOK/S · 1% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
2999
TOK/S · 1% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
2916
TOK/S · 1% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
2762
TOK/S · 1% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
2099
TOK/S · 1% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
2099
TOK/S · 1% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
2018
TOK/S · 1% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
1848
TOK/S · 1% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1848
TOK/S · 1% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
1749
TOK/S · 1% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
1749
TOK/S · 1% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
1749
TOK/S · 1% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
1693
TOK/S · 1% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
1418
TOK/S · 1% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
1399
TOK/S · 1% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
1296
TOK/S · 1% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
1206
TOK/S · 1% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
1117
TOK/S · 1% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
972
TOK/S · 1% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
954
TOK/S · 1% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
875
TOK/S · 1% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
875
TOK/S · 1% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
875
TOK/S · 1% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
875
TOK/S · 1% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
807
TOK/S · 1% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
807
TOK/S · 1% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
807
TOK/S · 1% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
777
TOK/S · 1% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
739
TOK/S · 1% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
739
TOK/S · 1% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
700
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 1550.

WHAT THIS CARD IS WORTH

Data Center GPU Max 1550 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 GB96 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 1550128 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 1550 — 128 GB VRAM.

▸ DATA CENTER GPU MAX 1550 SPEC
BRAND
Intel
VRAM
128 GB HBM2e
BANDWIDTH
3280 GB/s
FP16 COMPUTE
52.4 TFLOPS
FP32 COMPUTE
52.4 TFLOPS
TDP
600 W
ARCHITECTURE
Generation 12.5
▸ AI CAPABILITY
397/ 449 models @ Q4

With 128 GB VRAM and 3280 GB/s bandwidth, this GPU handles models up to 180B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~375 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR DATA CENTER GPU MAX 1550
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 GB2742.4
Mixtral-8x22B140.6B86.4 GB2731.9
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 GB8753.2
Mistral-Large 123B123B75.7 GB933.5
Devstral 2 123B123B75.7 GB938.1
Qwen 3.5 122B A10B122B75.1 GB10556.8
Nemotron 3 Super 120B120B73.8 GB8757.3
Mistral Small 4 119B119B73.2 GB16150.2
GPT-OSS 120B117B72.0 GB20654.1
Command A 111B111B68.3 GB927.6
GLM 4.5 Air110B67.7 GB8751.0
Qwen 1.5 110B110B67.7 GB1033.4
Llama 4 Scout 17B-16E109B67.1 GB6233.9
Cogito v2 109B MoE109B67.1 GB62—