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GPU Specs

GPU specs define hardware characteristics used for roofline analysis, performance modeling, and comparison. Wafer includes built-in specs for common GPUs and supports custom configurations.

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Quick Start

# List available specs
wafer config specs list

# Show spec details
wafer config specs show H100

# Set default spec
wafer config specs default H100

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Commands

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wafer config specs list

List all available GPU specifications: 
wafer config specs list
Output: 
GPU Specifications:

NVIDIA:
  Name       Memory    BW (TB/s)   FP16 (TFLOPS)   TDP
  H100       80GB      3.35        989.4           700W
  H200       141GB     4.80        989.4           700W
  A100       80GB      2.04        312.0           400W
  B200       192GB     8.00        2250.0          1000W
  RTX4090    24GB      1.01        165.2           450W

AMD:
  Name       Memory    BW (TB/s)   FP16 (TFLOPS)   TDP
  MI300X     192GB     5.30        1307.4          750W
  MI250X     128GB     3.28        383.0           560W
  MI210      64GB      1.64        181.0           300W

* = default

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wafer config specs show

Show detailed specifications for a GPU: 
wafer config specs show <gpu-name>
Example: 
wafer config specs show H100
Output: 
NVIDIA H100 SXM5 (80GB)

Memory:
  Capacity: 80 GB HBM3
  Bandwidth: 3.35 TB/s
  Bus Width: 5120-bit

Compute:
  FP64: 33.5 TFLOPS
  FP32: 66.9 TFLOPS
  FP16: 989.4 TFLOPS (Tensor Core)
  BF16: 989.4 TFLOPS (Tensor Core)
  INT8: 1978.9 TOPS (Tensor Core)
  FP8: 1978.9 TFLOPS (Tensor Core)

Architecture:
  SMs: 132
  CUDA Cores: 16896
  Tensor Cores: 528 (4th gen)
  L2 Cache: 50 MB
  Registers/SM: 65536

Roofline:
  Ridge Point (FP16): 295.3 FLOP/byte
  Ridge Point (FP32): 20.0 FLOP/byte

Power:
  TDP: 700W

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wafer config specs add

Add a custom GPU specification: 
wafer config specs add <name> [OPTIONS]
Options:
OptionDescription
--memoryMemory capacity (e.g., “80GB”)
--bandwidthMemory bandwidth (e.g., “3.35TB/s”)
--fp16FP16 peak TFLOPS
--fp32FP32 peak TFLOPS
--tdpThermal design power (watts)
Example: 
wafer config specs add my-custom-gpu \
  --memory 48GB \
  --bandwidth 2.0TB/s \
  --fp16 400 \
  --fp32 200 \
  --tdp 350

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wafer config specs remove

Remove a custom specification: 
wafer config specs remove <name>
Built-in GPU specs cannot be removed, only custom specs.

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wafer config specs default

Set the default GPU for analysis: 
wafer config specs default <name>
The default is used when no --gpu flag is specified: 
# Set default
wafer config specs default H100

# Now roofline uses H100 automatically
wafer roofline --bytes 1e9 --flops 1e12 --time-ms 0.5

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Using Specs

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Roofline Analysis

# Use specific GPU
wafer roofline --gpu H100 --bytes 1e9 --flops 1e12 --time-ms 0.5

# Use default GPU
wafer roofline --bytes 1e9 --flops 1e12 --time-ms 0.5

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Baseline Discovery

wafer baseline run "torch.matmul(A, B)" \
  --shape A=1024,1024 \
  --shape B=1024,1024 \
  --hardware H100

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Performance Comparison

Compare same workload across GPUs: 
# Analyze for H100
wafer roofline --gpu H100 --bytes 2e9 --flops 4e12 --time-ms 1.0

# Analyze for A100
wafer roofline --gpu A100 --bytes 2e9 --flops 4e12 --time-ms 1.5

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Built-in Specifications

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NVIDIA GPUs

GPUGenerationMemoryPeak FP16
B200Blackwell192GB HBM3e2250 TFLOPS
H200Hopper141GB HBM3e989 TFLOPS
H100Hopper80GB HBM3989 TFLOPS
A100Ampere80GB HBM2e312 TFLOPS
RTX 4090Ada24GB GDDR6X165 TFLOPS

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AMD GPUs

GPUGenerationMemoryPeak FP16
MI300XCDNA 3192GB HBM31307 TFLOPS
MI250XCDNA 2128GB HBM2e383 TFLOPS
MI210CDNA 264GB HBM2e181 TFLOPS

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Custom Spec File

Create specs from a YAML file: 
# my-gpu.yaml
name: Custom GPU
vendor: NVIDIA
memory:
  capacity_gb: 80
  bandwidth_tb_s: 3.0
compute:
  fp16_tflops: 500
  fp32_tflops: 250
  fp64_tflops: 125
power:
  tdp_watts: 400

wafer config specs add --from-file my-gpu.yaml

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Next Steps

Roofline Analysis

Use specs for roofline analysis.

Baseline Discovery

Use specs with baseline.

Targets

Configure GPU targets.

Workspaces

Access cloud GPUs.