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Never under estimate the value of a large dedicated server. For ~$100/mo, you can get: AMD Ryzen 9 7950 16-cores 128GB RAM 4TB NVMe https://www.hetzn

Never under estimate the value of a large dedicated server. For ~$100/mo, you can get: AMD Ryzen 9 7950 16-cores 128GB RAM 4TB NVMe https://www.hetzner.com/dedicated-rootserver/ax102/configurator#/ https://www.hetzner.com/dedicated-rootserver/ax102/configura... For ~$800/mo, you can get: AMD EPYC 7763 64-cores 256GB RAM (up to 1TB) 4TB NVME (up to 12TB) https://www.ovhcloud.com/en/bare-metal/scale/scale-7/ https://www.ovhcloud.com/en/bare-metal/scale/scale-7/

jasfi 3y ago
[flagged]
- lakomen 3y ago
  Terrible offers. Only a single HDD/SSD. Also you're posting affiliate links
  - jasfi 3y ago
    Those offers are optimized for CPU and memory. You can always add storage. Tell me where you can get a better offer. An affiliate link? So what?
andrewstuart 3y ago
Ionos have pretty good value dedicated servers too: https://www.ionos.com/servers/dedicated-servers https://www.ionos.com/servers/dedicated-servers
Nevermark 3y ago
Along those lines, the graph didn't show Cerebras 2.6 TRILLION transistor wafer scale WSE-2 deep learning accelerator. Be interesting to see if wafer scale becomes more common. I would think wafer scale CPU core farms would help cloud providers deliver greater one-big-server goodness. 850,000 cores 40GB of ram distributed among cores 220Pb/s of memory bandwidth Result: 7,500 trillion FP16 operations per second Product page: https://www.cerebras.net/product-chip/ https://www.cerebras.net/product-chip/ Tech dive: https://www.techinsights.com/blog/cerebras-dives-wse-architecture https://www.techinsights.com/blog/cerebras-dives-wse-archite...
- saagarjha 3y ago
  Is it just me or is that very little RAM? What kind of workloads would you run on it?
  - magicalhippo 3y ago
    The accelerator contains 40GB of SRAM It's cache or scratchpad memory. Keep in mind this is "on chip", most systems, including this one, have additional memory off chip in the form of DRAM sticks. edit: For comparison, the NVIDIA A100 has up to 164kB of shared memory per SM while the wafer beast has about 47kB per core if you divide it out.

jasfi 3y ago

[flagged]

lakomen 3y ago
Terrible offers. Only a single HDD/SSD. Also you're posting affiliate links
- jasfi 3y ago
  Those offers are optimized for CPU and memory. You can always add storage. Tell me where you can get a better offer. An affiliate link? So what?

andrewstuart 3y ago

Ionos have pretty good value dedicated servers too: https://www.ionos.com/servers/dedicated-servers https://www.ionos.com/servers/dedicated-servers

Nevermark 3y ago

Along those lines, the graph didn't show Cerebras 2.6 TRILLION transistor wafer scale WSE-2 deep learning accelerator. Be interesting to see if wafer scale becomes more common. I would think wafer scale CPU core farms would help cloud providers deliver greater one-big-server goodness. 850,000 cores 40GB of ram distributed among cores 220Pb/s of memory bandwidth Result: 7,500 trillion FP16 operations per second Product page: https://www.cerebras.net/product-chip/ https://www.cerebras.net/product-chip/ Tech dive: https://www.techinsights.com/blog/cerebras-dives-wse-architecture https://www.techinsights.com/blog/cerebras-dives-wse-archite...

saagarjha 3y ago
Is it just me or is that very little RAM? What kind of workloads would you run on it?
- magicalhippo 3y ago
  The accelerator contains 40GB of SRAM It's cache or scratchpad memory. Keep in mind this is "on chip", most systems, including this one, have additional memory off chip in the form of DRAM sticks. edit: For comparison, the NVIDIA A100 has up to 164kB of shared memory per SM while the wafer beast has about 47kB per core if you divide it out.