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Radeon PRO W7900 (48GB)

The Radeon PRO W7900 (48GB) provides 48 GB of memory with 864 GB/s bandwidth, supporting local inference via ROCm and Vulkan acceleration.

Memory

48 GB

Usable Memory Ceiling

48 GB

Bandwidth

864 GB/s

Max Inference Class

32B Models (Q4)

TDP

295W

VRAM Scaling by Context Length

Estimated memory requirements as context length increases. Curves crossing above the reference line indicate Out of Memory (OOM).

DeepSeek-R1 7B (7B)Falcon2-11B (11B)Qwen3-30B-A3B (30B (3B active))Ling 3.0 Flash VL (124B (5.5B active))

Precision:

Best Models to Run on Radeon PRO W7900 (48GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on Radeon PRO W7900 (48GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#48

Qwen 3.8 27B

27B

INT4
Q8

41 tok/s

24 tok/s

~2.8s

~2.8s

#83

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8

132 tok/s

100 tok/s

~555 ms

~558 ms

#93

Qwen3.5-27B

27B

INT4
Q8

41 tok/s

24 tok/s

~2.8s

~2.8s

#105

Sarvam-30B

32B (2.4B active)

INT4
Q8

157 tok/s

129 tok/s

~389 ms

~391 ms

#106

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8

146 tok/s

116 tok/s

~453 ms

~455 ms

#107

Nemotron 3.5 Bolt 74B A9B

74B

INT4

16 tok/s

~8.3s

#110

Muse Glimmer 30B

30B

INT4
Q8

37 tok/s

22 tok/s

~3.1s

~3.1s

#111

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

4 tok/s

2 tok/s

1 tok/s

~30.6s

~30.8s

~31.2s

#112

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

52 tok/s

37 tok/s

22 tok/s

~1.5s

~1.5s

~1.5s

#117

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8

138 tok/s

105 tok/s

~508 ms

~511 ms

#125

Qwen3.5-9B

9B

INT4
Q8
FP16

94 tok/s

62 tok/s

35 tok/s

~961 ms

~966 ms

~979 ms

#126

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8

146 tok/s

116 tok/s

~453 ms

~455 ms

#128

K2 Horizon 7B

7B

INT4
Q8
FP16

110 tok/s

75 tok/s

44 tok/s

~754 ms

~758 ms

~767 ms

#135

Gemma 4 12B

11.95B

INT4
Q8
FP16

59 tok/s

42 tok/s

25 tok/s

~1.3s

~1.3s

~1.3s

#140

Qwen3.5-4B

4B

INT4
Q8
FP16

148 tok/s

108 tok/s

68 tok/s

~445 ms

~447 ms

~453 ms

#143

GPT-OSS 20B

21B (3.6B active)

INT4
Q8
FP16

78 tok/s

69 tok/s

46 tok/s

~481 ms

~483 ms

~572 ms

#144

GLM-4V

9B

INT4
Q8
FP16

72 tok/s

52 tok/s

32 tok/s

~964 ms

~969 ms

~982 ms

#150

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

36 tok/s

22 tok/s

11 tok/s

~3.0s

~3.0s

~3.1s

#153

Qwen3-30B-A3B

30B (3B active)

INT4
Q8

148 tok/s

117 tok/s

~444 ms

~446 ms

#158

Granite 4.2 8B

8B

INT4
Q8
FP16

102 tok/s

68 tok/s

39 tok/s

~856 ms

~861 ms

~872 ms

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

32B Models

8-bit

Q8

32B Models

16-bit

FP16

14B Models

Fine-Tuning Capacity

Hardware capacity for local training and adapter fine-tuning.

Context Length:

QLoRA

4-bit

32B Models

LoRA

16-bit

14B Models

Full Parameter Training

7B-8B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on Radeon PRO W7900 (48GB).

Inference Sweet Spot

Optimized for 14B models at uncompressed or Q8 precision, and 32B models at Q4 with up to 32k context.

Bandwidth & Speed Profile

With 864 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 192 tok/s on an 8B Q4 model and 23 tok/s on a 70B Q4 model.

Frequently Asked Questions