@NVIDIARTXSpark it should be more like a DGX Workstation, then it would make more sense... I have 2 sparks at home... on first 1 work on second i run qwen ... but i still use codex/calude code on first one lol ...
Local AI hardware = capacity × bandwidth × software stack
- Capacity tells you what fits
- Bandwidth tells you how hard the box can breathe
- The software stack tells you how much of the spec sheet you can actually cash out.
Hardware by Memory Bandwidth
- Mac Studio M3 Ultra: up to 512GB @ 819 GB/s
- RTX PRO 6000 Blackwell: 96GB @ 1792 GB/s
- RTX 5090: 32GB @ 1792 GB/s
- RTX 4090: 24GB @ 1008 GB/s
- RX 7900 XTX: 24GB @ 960 GB/s
- Radeon PRO W7900: 48GB @ 864 GB/s
- AMD Radeon AI PRO R9700: 32GB @ 640 GB/s
- Intel Arc Pro B65: 32GB @ ~608 GB/s
- Tenstorrent Wormhole n300: 24GB @ 576 GB/s
- Tenstorrent Blackhole p150: 32GB @ 512 GB/s + 800G
- MacBook Pro M5 Max: 460-614 GB/s
- MacBook Pro M5 Pro: 307 GB/s
- DGX Spark: 128GB @ 273 GB/s (coherent + CUDA)
- Mac mini M4 Pro: 273 GB/s
- Ryzen AI Max / Strix Halo: ~256 GB/s (~96GB usable GPU)
- MacBook Air M5: 153 GB/s
- Snapdragon X2 Elite: 152-228 GB/s
- Intel Lunar Lake: 136 GB/s
- Snapdragon X Elite: 135 GB/s
- Mac mini M4: 120 GB/s
- Arc Pro B60: 24GB @ ~456 GB/s
Verdict
- GPUs are still the bandwidth kings
- Apple wins: stupid amounts of memory, don’t want to shard across GPUs
- Apple loses: when raw tokens/sec & concurrency matter more
- DGX Spark: coherent memory + NVIDIA stack
- Strix Halo / Ryzen AI Max: first real x86 unified-memory contender
- Tenstorrent: fully OSS stack, excited to see this mature
Fitting ≠ serving
Even if it fits, you still pay for
- bandwidth during decode
- KV cache growth
- dequantization
- batching + concurrency
- scheduler quality
- framework overhead
The only mental model that matters:
1. What must fit?
2. What bandwidth tier do I need?
3. What software stack can actually deliver it?
In short:
- NVIDIA → fastest raw speed
- Apple Studio M3 Ultra → biggest one-box memory
- Strix Halo → first real x86 unified
- DGX Spark → coherent NVIDIA dev appliance
- AMD / Intel Arc → rising alternatives
- Tenstorrent → fully opensource stack
Do ask: “which bottleneck am I buying?”
Not: “which hardware is best?”
Today we’re wiring @virtuals_io ACP into the Chain Insights CLI.
Goal: make $CIA usable from agent commerce flows, not just manual investigations.
Boring integration work, but this is how agent services become real: clean capability surfaces, safer execution paths, and evidence you can inspect.
solana:3iQL8BFS2vE7mww4ehAqQHAsbmRNCrPxizWAT2Zfyr9y
Small release, big activation detail: `[email protected]` adds `cia update`.
If an investigator or agent builder starts from an old CLI, the tool points them back to latest before the first query.
Building Chain Insights in public for $VIRTUAL. Run `cia update` before tests.
i tested localy nvidia/Qwen3.6-35B-A3B-NVFP4 on my spark:
One Spark can decode around ~100-110 output tok/s after a 256K prompt is ingested, but you pay ~176 seconds before the first token for a full 256K context #spark#nvidia#gb10