CPU types

Available CPU instance types for Flash endpoints.

Flash provides access to CPU-only compute instances for workloads that don’t require GPU acceleration. This reference lists all available CPU instance types.

Using CPU instances

Specify a CPU instance using the cpu parameter. You can use either a string shorthand or the CpuInstanceType enum:

from runpod_flash import Endpoint, CpuInstanceType

# String shorthand
@Endpoint(name="data-processor", cpu="cpu5c-4-8")
async def process(data: dict) -> dict:
    ...

# Using enum
@Endpoint(name="data-processor", cpu=CpuInstanceType.CPU5C_4_8)
async def process(data: dict) -> dict:
    ...

Available CPU instance types

CPU instances are organized by generation and optimization profile.

5th generation compute-optimized

Latest generation, optimized for compute-intensive workloads:

CpuInstanceType	ID	vCPU	RAM	Best For
CPU5C_1_2	cpu5c-1-2	1	2GB	Lightweight APIs, simple tasks
CPU5C_2_4	cpu5c-2-4	2	4GB	Small APIs, data validation
CPU5C_4_8	cpu5c-4-8	4	8GB	General APIs, data processing
CPU5C_8_16	cpu5c-8-16	8	16GB	Heavy processing, parallel tasks

3rd generation compute-optimized

Balanced compute focus:

CpuInstanceType	ID	vCPU	RAM	Best For
CPU3C_1_2	cpu3c-1-2	1	2GB	Basic endpoints, webhooks
CPU3C_2_4	cpu3c-2-4	2	4GB	Simple data processing
CPU3C_4_8	cpu3c-4-8	4	8GB	Moderate workloads
CPU3C_8_16	cpu3c-8-16	8	16GB	CPU-intensive tasks

3rd generation general purpose

Balanced CPU and memory:

CpuInstanceType	ID	vCPU	RAM	Best For
CPU3G_1_4	cpu3g-1-4	1	4GB	Memory-light tasks
CPU3G_2_8	cpu3g-2-8	2	8GB	General workloads
CPU3G_4_16	cpu3g-4-16	4	16GB	Memory-intensive processing
CPU3G_8_32	cpu3g-8-32	8	32GB	High-memory workloads

Common configurations

APIs and webhooks

# Lightweight API
@Endpoint(name="webhook", cpu="cpu5c-2-4")
async def handle_webhook(data: dict) -> dict:
    ...

# Production API
@Endpoint(name="api", cpu="cpu5c-4-8", workers=(1, 10))
async def handle_request(data: dict) -> dict:
    ...

Data processing

# Light processing
@Endpoint(name="processor", cpu="cpu3g-2-8")  # More RAM per vCPU
async def process(data: dict) -> dict:
    ...

# Heavy processing
@Endpoint(name="heavy-processor", cpu="cpu5c-8-16")
async def heavy_process(data: dict) -> dict:
    ...

Memory-intensive tasks

# High memory requirement
@Endpoint(name="memory-worker", cpu="cpu3g-8-32")  # 8 vCPU, 32GB RAM
async def process_large_data(data: dict) -> dict:
    ...

Load-balanced CPU API

from runpod_flash import Endpoint

api = Endpoint(
    name="cpu-api",
    cpu="cpu5c-4-8",
    workers=(1, 10)
)

@api.post("/process")
async def process(data: dict) -> dict:
    return {"result": "processed"}

@api.get("/health")
async def health():
    return {"status": "ok"}

Container disk sizing

CPU endpoints automatically adjust container disk size based on instance limits:

• CPU3G and CPU3C instances: vCPU count × 10GB (e.g., 2 vCPU = 20GB)
• CPU5C instances: vCPU count × 15GB (e.g., 4 vCPU = 60GB)

If you specify a custom size via PodTemplate that exceeds the instance limit, deployment will fail with a validation error.