FitMyLLM
AMD· CDNA3

AMD Instinct MI300A

Running LLMs on the Instinct MI300A — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $12,000 buys

VRAM
120 GB
FLAGSHIP
BANDWIDTH
5300
GB/S
MODELS Q4
395/449
88%
7B Q4 SPEED
~673
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
~1570
TOK/S
7B
~673
TOK/S
14B
~337
TOK/S
32B
~147
TOK/S
70B
~67
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
120 GB
BANDWIDTH
5300 GB/s
FP16 COMPUTE
980.6 TFLOPS
TDP
550W
MEMORY
HBM3
ARCHITECTURE
CDNA3
PCIE
Gen 5 x16
MSRP
$12,000
395
FAST MODELS · >30 TOK/S
Real-time chat speed
395
USABLE · >10 TOK/S
Comfortable for all tasks
395
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying Instinct MI300A 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· 395
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
52346
TOK/S · 0% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
34897
TOK/S · 0% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
33651
TOK/S · 0% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
33651
TOK/S · 0% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
18403
TOK/S · 1% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
17449
TOK/S · 1% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
15446
TOK/S · 1% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
14063
TOK/S · 1% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
14063
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
14063
TOK/S · 1% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
13460
TOK/S · 1% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
13086
TOK/S · 1% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
12398
TOK/S · 1% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
9422
TOK/S · 1% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
9422
TOK/S · 1% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
9060
TOK/S · 1% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
8294
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
8294
TOK/S · 1% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
7852
TOK/S · 1% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
7852
TOK/S · 1% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
7852
TOK/S · 1% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
7599
TOK/S · 1% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
6366
TOK/S · 1% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
6281
TOK/S · 1% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
5816
TOK/S · 1% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
5415
TOK/S · 1% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
5012
TOK/S · 1% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
4362
TOK/S · 1% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
4283
TOK/S · 1% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
3926
TOK/S · 1% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
3926
TOK/S · 1% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
3926
TOK/S · 1% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
3926
TOK/S · 1% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
3624
TOK/S · 1% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
3624
TOK/S · 1% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
3624
TOK/S · 1% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
3490
TOK/S · 1% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
3318
TOK/S · 1% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
3318
TOK/S · 1% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
3141
TOK/S · 5% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Instinct MI300A.

WHAT THIS CARD IS WORTH

Instinct MI300A holds 395 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Falcon 180B at IQ4_XS.

TOKENS/SEC PER $100
4.7
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
1.00 GB
what memory costs on this card
THE BIGGEST IT TAKES
Falcon 180B180B · IQ4_XS104.1 GB34 tok/sEST
dots.llm1.inst 142.8B142.8B · Q5_K_S105.8 GB35 tok/sEST
WizardLM 2 8x22B141B · Q5_K_M103.8 GB124 tok/sEST
Mixtral-8x22B140.6B · Q5_K_M103.6 GB123 tok/sEST
DBRX 132B132B · Q5_K_M97.2 GB134 tok/sEST
Mistral Medium 3.5128B · Q5_K_M95.1 GB38 tok/sEST
Pixtral Large 124B124B · Q6_K105.6 GB34 tok/sEST
Nemotron 3 Super 120B-A12B123.61B · Q6_K104.2 GB303 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
Instinct MI300A120 GB$12,0004.7 tok/s per $100

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

AMD Instinct MI300A — 120 GB VRAM.

INSTINCT MI300A SPEC
BRAND
AMD
VRAM
120 GB HBM3
BANDWIDTH
5300 GB/s
FP16 COMPUTE
980.6 TFLOPS
FP32 COMPUTE
122.6 TFLOPS
TDP
550 W
ARCHITECTURE
CDNA3
MSRP
$12000
▸ AI CAPABILITY
395/ 449 models @ Q4

With 120 GB VRAM and 5300 GB/s bandwidth, this GPU handles models up to 142.8B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~606 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR INSTINCT MI300A
395 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
dots.llm1.inst 142.8B142.8B87.8 GB33
WizardLM 2 8x22B141B86.7 GB12142.4
Mixtral-8x22B140.6B86.4 GB12031.9
DBRX 132B132B81.2 GB13146.3
Mistral Medium 3.5128B78.7 GB3746.6
Pixtral Large 124B124B76.3 GB3839.3
Nemotron 3 Super 120B-A12B123.61B76.0 GB39353.2
Mistral-Large 123B123B75.7 GB3833.5
Devstral 2 123B123B75.7 GB3838.1
Qwen 3.5 122B A10B122B75.1 GB47156.8
Nemotron 3 Super 120B120B73.8 GB39357.3
Mistral Small 4 119B119B73.2 GB72550.2
GPT-OSS 120B117B72.0 GB92454.1
Command A 111B111B68.3 GB4227.6
GLM 4.5 Air110B67.7 GB39351.0
Qwen 1.5 110B110B67.7 GB4333.4
Llama 4 Scout 17B-16E109B67.1 GB27733.9
Cogito v2 109B MoE109B67.1 GB277
Ling 2.6 Flash107.49B66.2 GB63736.8
Sarvam 105B105B64.7 GB4548.0