datacenter
租用 AMD MI325.
CDNA 3
256GB VRAM
1000W
From $3.08/hr
Per hour
$3.08
Per day
$73.90
Per week
$517.27
Per month
$2217
Price history
Daily median across providers.
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Buy vs rent
Should you rent or own?
Your usage
Rent on cloud
Per day
—
Per month
—
at $3.08/hr cheapest provider rate
Own on-prem
Electricity per day
—
1000W TDP
Rent breakeven on $20,000 MSRP
— days
· — months
AI models that fit
See all 86 →
Run these on this GPU.
- Llama 3.2 90B Vision 1× · fp16
- Qwen: Qwen3 Next 80B A3B Thinking 1× · fp16
- Qwen: Qwen3 Next 80B A3B Instruct 1× · fp16
Cloud instances
See all 12 →
Hyperscaler bundles.
Pre-configured on 1 clouds — from $3.08/hr total
($2.98/hr per GPU).
FAQ
Frequently asked.
What AI models can I run on a AMD MI325?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 256GB of VRAM.
What's the VRAM minimum to run a model on the AMD MI325?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the AMD MI325 or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.
Cloud instance options
Pre-configured instances on hyperscalers.
Whole-instance bundles (GPU + vCPU + RAM + disk) on the major clouds. Per-GPU rate often drops as the count rises. View = spec page · Launch = sign up (affiliate).
Cheapest bundle
$3.08/hr
Lowest $/hr per GPU
$2.98/hr
Providers
1
Instance shapes
12
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| 1xMI325.16v.256m | 1× | 16 | 256 GB | — | $3.08/hr | $3.08/hr | ||
| 1xMI325.16v.256m | 1× | 16 | 256 GB | — | $3.08/hr | $3.08/hr | ||
| 1xMI325.16v.256m | 1× | 16 | 256 GB | — | $3.08/hr | $3.08/hr | ||
| 2xMI325.32v.512m | 2× | 32 | 512 GB | — | $6.09/hr | $3.05/hr | ||
| 2xMI325.32v.512m | 2× | 32 | 512 GB | — | $6.10/hr | $3.05/hr | ||
| 2xMI325.32v.512m | 2× | 32 | 512 GB | — | $6.10/hr | $3.05/hr | ||
| 4xMI325.64v.1024m | 4× | 64 | 768 GB | — | $12.05/hr | $3.01/hr | ||
| 4xMI325.64v.1024m | 4× | 64 | 768 GB | — | $12.06/hr | $3.01/hr | ||
| 4xMI325.64v.1024m | 4× | 64 | 768 GB | — | $12.07/hr | $3.02/hr | ||
| 8xMI325.128v.2048m | 8× | 128 | 1536 GB | — | $23.87/hr | $2.98/hr | ||
| 8xMI325.128v.2048m | 8× | 128 | 1536 GB | — | $23.88/hr | $2.98/hr | ||
| 8xMI325.128v.2048m | 8× | 128 | 1536 GB | — | $23.90/hr | $2.99/hr |
Looking for the cheapest rate?
Hyperscaler bundles include managed networking + SLAs. Raw per-GPU rental on P2P marketplaces is typically 3–10× cheaper.
See raw rental rates on the Overview tab →
FAQ
Cloud instances — common questions.
What AI models can I run on a AMD MI325?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 256GB of VRAM.
What's the VRAM minimum to run a model on the AMD MI325?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the AMD MI325 or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.
AI models
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the AMD MI325.
Llama 3.2 90B Vision
by Meta AI
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
90B
Context
128K tokens
Qwen: Qwen3 Next 80B A3B Thinking
by Alibaba (Qwen Team)
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
80B
Context
262K tokens
Qwen: Qwen3 Next 80B A3B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
80B
Context
262K tokens
Qwen3 Next 80B A3b Instruct Fp8
by Alibaba (Qwen Team)
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
80B
nim/meta/llama-3.2-90b-vision-instruct
by Meta AI
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
90B
Context
16K tokens
Tencent: Hunyuan A13B Instruct
by Tencent
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
80B
Context
131K tokens
?
FP16
ByteDance Seed: Seed 1.6
by Bytedance Seed
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
200B
Context
262K tokens
?
FP16
Seed-1.8
by Bytedance
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
200B
Context
256K tokens
StepFun: Step 3.5 Flash
by Stepfun
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
199B
Context
262K tokens
Llama 3.1 405B
by Meta AI
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
405B
Context
128K tokens
GLM-4.5
by Zhipu AI
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
355B (32B active)
Context
128K tokens
Hunyuan-Large
by Tencent
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
389B (52B active)
Context
256K tokens
Qwen: Qwen3.5 397B A17B
by Alibaba (Qwen Team)
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
397B
Context
262K tokens
?
FP16
Nous: Hermes 4 405B
by Nous Research
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
405B
Context
131K tokens
?
FP16
Nous: Hermes 3 405B Instruct
by Nous Research
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
405B
Context
131K tokens
Meta Llama 3.1 405B Instruct
by Meta AI
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
405B
Context
4K tokens
GLM-5
by Zhipu AI
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
355B
Context
203K tokens
GLM-4.6
by Zhipu AI
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
355B
Context
203K tokens
Xiaomi: MiMo-V2.5
by Xiaomi
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
315B
Context
1M tokens
Arcee AI: Trinity Large Thinking
by Arcee Ai
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
399B
Context
262K tokens
Meta: Llama 4 Maverick
by Meta AI
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
402B
Context
1M tokens
Qwen3.5 397B A17B Lora
by Alibaba (Qwen Team)
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
397B
Context
262K tokens
DeepSeek V3
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
671B (37B active)
Context
128K tokens
DeepSeek R1
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
671B (37B active)
Context
128K tokens
Deep Cogito: Cogito v2.1 671B
by Deepcogito
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
671B
Context
128K tokens
DeepSeek: DeepSeek V3.2 Exp
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
671B
Context
164K tokens
DeepSeek: R1 0528
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
671B
Context
164K tokens
DeepSeek-V3-0324
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
671B
Context
164K tokens
DeepSeek: DeepSeek V3.2
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
689B
Context
131K tokens
DeepSeek: DeepSeek V3.1 Terminus
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V3.1
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V3 0324
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V3
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
689B
Context
164K tokens
?
FP8
ByteDance Seed: Seed 1.6
by Bytedance Seed
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
200B
Context
262K tokens
?
FP8
Seed-1.8
by Bytedance
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
200B
Context
256K tokens
StepFun: Step 3.5 Flash
by Stepfun
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
199B
Context
262K tokens
Llama 3.1 405B
by Meta AI
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
405B
Context
128K tokens
GLM-4.5
by Zhipu AI
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
355B (32B active)
Context
128K tokens
Hunyuan-Large
by Tencent
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
389B (52B active)
Context
256K tokens
Qwen: Qwen3.5 397B A17B
by Alibaba (Qwen Team)
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
397B
Context
262K tokens
?
FP8
Nous: Hermes 4 405B
by Nous Research
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
405B
Context
131K tokens
?
FP8
Nous: Hermes 3 405B Instruct
by Nous Research
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
405B
Context
131K tokens
Meta Llama 3.1 405B Instruct
by Meta AI
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
405B
Context
4K tokens
GLM-5
by Zhipu AI
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
355B
Context
203K tokens
GLM-4.6
by Zhipu AI
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
355B
Context
203K tokens
Xiaomi: MiMo-V2.5
by Xiaomi
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
315B
Context
1M tokens
Arcee AI: Trinity Large Thinking
by Arcee Ai
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
399B
Context
262K tokens
Meta: Llama 4 Maverick
by Meta AI
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
402B
Context
1M tokens
Qwen3.5 397B A17B Lora
by Alibaba (Qwen Team)
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
397B
Context
262K tokens
DeepSeek V3
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
671B (37B active)
Context
128K tokens
DeepSeek R1
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
671B (37B active)
Context
128K tokens
Deep Cogito: Cogito v2.1 671B
by Deepcogito
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
671B
Context
128K tokens
DeepSeek: DeepSeek V3.2 Exp
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
671B
Context
164K tokens
DeepSeek: R1 0528
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
671B
Context
164K tokens
DeepSeek-V3-0324
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
671B
Context
164K tokens
DeepSeek: DeepSeek V3.2
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
689B
Context
131K tokens
DeepSeek: DeepSeek V3.1 Terminus
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V3.1
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V3 0324
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V3
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V4 Pro
by DeepSeek
Required GPUs
8× AMD MI325
Total VRAM
2048 GB
Parameters
1602B
Context
1M tokens
Llama 3.1 405B
by Meta AI
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
405B
Context
128K tokens
GLM-4.5
by Zhipu AI
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
355B (32B active)
Context
128K tokens
Hunyuan-Large
by Tencent
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
389B (52B active)
Context
256K tokens
Qwen: Qwen3.5 397B A17B
by Alibaba (Qwen Team)
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
397B
Context
262K tokens
?
INT4
Nous: Hermes 4 405B
by Nous Research
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
405B
Context
131K tokens
?
INT4
Nous: Hermes 3 405B Instruct
by Nous Research
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
405B
Context
131K tokens
Meta Llama 3.1 405B Instruct
by Meta AI
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
405B
Context
4K tokens
GLM-5
by Zhipu AI
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
355B
Context
203K tokens
GLM-4.6
by Zhipu AI
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
355B
Context
203K tokens
Xiaomi: MiMo-V2.5
by Xiaomi
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
315B
Context
1M tokens
Arcee AI: Trinity Large Thinking
by Arcee Ai
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
399B
Context
262K tokens
Meta: Llama 4 Maverick
by Meta AI
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
402B
Context
1M tokens
Qwen3.5 397B A17B Lora
by Alibaba (Qwen Team)
Required GPUs
1× AMD MI325
Total VRAM
256 GB
Parameters
397B
Context
262K tokens
DeepSeek V3
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
671B (37B active)
Context
128K tokens
DeepSeek R1
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
671B (37B active)
Context
128K tokens
Deep Cogito: Cogito v2.1 671B
by Deepcogito
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
671B
Context
128K tokens
DeepSeek: DeepSeek V3.2 Exp
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
671B
Context
164K tokens
DeepSeek: R1 0528
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
671B
Context
164K tokens
DeepSeek-V3-0324
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
671B
Context
164K tokens
DeepSeek: DeepSeek V3.2
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
689B
Context
131K tokens
DeepSeek: DeepSeek V3.1 Terminus
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V3.1
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V3 0324
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V3
by DeepSeek
Required GPUs
2× AMD MI325
Total VRAM
512 GB
Parameters
689B
Context
164K tokens
DeepSeek: DeepSeek V4 Pro
by DeepSeek
Required GPUs
4× AMD MI325
Total VRAM
1024 GB
Parameters
1602B
Context
1M tokens
Renting for inference?
Pair these models with the cheapest provider on the rental table.
See rental rates on the Overview tab →
FAQ
AI models on this GPU.
What AI models can I run on a AMD MI325?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 256GB of VRAM.
What's the VRAM minimum to run a model on the AMD MI325?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the AMD MI325 or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.