NVIDIA Tesla M60
4,096
CUDA Cores
16GB
VRAM
320
GB/s
Technical Specifications
4,096
CUDA Cores
557
Base MHz
1178
Boost MHz
16GB GDDR5
512-bit bus
Performance
9
FP32 TFLOPS
18
FP16 TFLOPS
300W
TDP
Cloud Availability
0
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | Maxwell (Unknown) |
| Release Date | 2015-08-30 |
| Launch Price | $4,500.00 |
| Process | 28nm |
| Transistors | 10.4B |
AI Features
none
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
10.5in
Length
4.4in
Width
2-slot
Height
About Tesla M60 GPU
The NVIDIA Tesla M60 was introduced in 2015 on the Maxwell architecture. Its specification combines 4,096 CUDA cores with 16GB of GDDR5, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 16GB memory capacity, the Tesla M60 is best evaluated for quantized language-model inference, image generation, and medium-sized training jobs. Always size the model, optimizer state, KV cache, and framework overhead together rather than choosing a GPU from core count alone.
GPUvec does not currently have an active hourly cloud listing for the Tesla M60. This is not a zero-dollar offer: it means no comparable provider price is available in the present dataset. Check the provider directory for availability or compare a related GPU with an active listing.
For software planning, the Tesla M60 combines 320 GB/s of memory bandwidth, CUDA compute capability the latest, and a 28nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
External Resources
Learn more about GPUs from these authoritative sources:
Official CUDA programming guide
NVIDIA GPU Specifications →Official NVIDIA GPU specs
TechPowerUp GPU Database →Comprehensive GPU specifications
CUDA Compute Capability Guide →GPU compute capability reference
What You Need to Know About the Tesla M60
Complete Specifications for the NVIDIA Tesla M60
The NVIDIA Tesla M60 uses the Maxwell architecture and combines 4,096 CUDA cores with 16GB of GDDR5. GPUvec records 320 GB/s memory bandwidth alongside architecture, release date, process node, accelerator features, and physical specifications so the page can be evaluated from source data instead of model-name assumptions.
Compare NVIDIA Tesla M60 Cloud Rental Prices per Hour
GPUvec currently has no comparable active hourly listing for this model; this means pricing is unavailable, not free. Provider rates can vary by region, host configuration, billing model, and availability. Use the provider directory and related-GPU links to compare an available alternative when this exact accelerator is not listed.
Is the NVIDIA Tesla M60 the Right GPU for Your AI Workload?
Start with the 16GB memory limit, then evaluate 320 GB/s memory bandwidth, compute features, and software compatibility for your workload. Model weights, KV cache, activations, optimizer state, and framework overhead all consume memory. Compare this page with related accelerators rather than assuming that a higher core count alone guarantees a better training or inference result.
Top GPUs for Training and Inference
| Category | Rank 1 | Rank 2 | Rank 3 |
|---|---|---|---|
| Best for Training | NVIDIA H200 | NVIDIA H100 | NVIDIA B200 |
| Best for Inference | NVIDIA A40 | NVIDIA A100 | NVIDIA A10 |
Compare GPU specifications and cloud instances to find the best GPU for your workload.