Hardware Deep Dive - Fixing Local Model Failures

Topics Covered

1. The Context Overflow Bug — Clarity (coding agent, Qwen3-Coder-30B) repeatedly hit context overflow errors mid-task. Root cause: an Ollama model definition labeled v-128k that capped context at 131,072 tokens, while the model natively supports 262,144 tokens. A config label from setup day became a hard limit by accident.

2. The Timeout Math — On February 18 at 11:15 AM, 146,760 tokens were prompted against a 131,072-token cap. Pre-fill speed ~400 tokens/sec means processing 146K tokens takes 6+ minutes. Timeout threshold was 5 minutes. Three consecutive hits — not crashes, not instability — perfectly explained by the arithmetic.

3. Qwen3-Coder Memory Architecture — Qwen3-Coder 30B is a Mixture-of-Experts model with only 4 KV attention heads (vs. 8 for LLaMA-3 8B). At 262K context: ~15 GB model weights + ~24 GB KV cache + OS overhead ≈ 44 GB total. A 64 GB unified memory machine has 20 GB of headroom. The hardware was fine the entire time.

4. Hardware Option 1: NVIDIA DGX Spark ($3,000) — GB10 Grace Blackwell chip, 128 GB LPDDR5X, 273 GB/s memory bandwidth. Counter-intuitively lower bandwidth than a Mac Studio M2 Ultra (~800 GB/s). Compensates via FP4 tensor cores (25–50 tok/s on 70B vs. 10–15 currently). Linux-only sidecar; link two units for $6K to run 405B+ models.

5. Hardware Option 2: Mac Studio M3 Ultra — $4,000 (192 GB) to $8–10K (512 GB). 819 GB/s bandwidth, 2.1× faster than M2 Ultra per Apple benchmarks. 20–32 tok/s on 70B models. 192 GB config enables simultaneous loading of 30B + 70B models with no swapping. 512 GB config is the only consumer path to running LLaMA 3.1 405B locally.

6. Hardware Option 3: AMD — Two stories:

   • Threadripper + dual RX 7900 XTX ($5–6K): Split VRAM problem, ROCm lags CUDA. Hard pass.
   • Ryzen AI Max+ 395 "Strix Halo" ($2,000–2,500): AMD's answer to Apple Silicon — CPU/GPU/NPU unified memory up to 128 GB LPDDR5X. Framework Desktop AMD or ASUS ROG Flow Z13. Memory bandwidth is 256 GB/s (256-bit bus), ~3× less than M2 Ultra — competitive speeds despite double the addressable RAM. Best budget path to 128 GB unified memory, period.
   • AMD MI300X ($25K, enterprise): 192 GB HBM3, 5.3 TB/s bandwidth, 80–120 tok/s on 70B. Mentioned for completeness; not a consumer purchase.

7. Workflow Strategy: Hybrid Local + Cloud — Heavy multi-file edits (5+ templates, large codebase refactors) represent ~20% of total workload. For non-private code: Devstral offers 262K native context on the free Mistral API tier; Gemini 2.5 Pro offers 1 million tokens. The right question isn't "how do I run my hardest jobs locally?" — it's "should my hardest jobs be local at all?"

8. The Fix — Config patch + new Ollama model definition to unlock the full 262K context window. Done live during the research. Zero cost.

Hardware Resources

• Apple Mac Mini - Recommended for local AI
• Raspberry Pi 5 - Budget option
• Ollama - Local LLM runtime

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Key Takeaways

1. A model label is not a capability ceiling. The Ollama model name qwen3-coder:30b-262k told the truth; the creation-time label did not. Always verify context window config against the model's actual spec.
2. Token generation is memory-bandwidth-bound, not compute-bound. The DGX Spark has less memory bandwidth than a Mac Studio. Bandwidth is the bottleneck — always check GB/s, not just GB.
3. Strix Halo (Ryzen AI Max+ 395) is the cheapest path to 128 GB unified memory. Nothing else comes close at under $3K. The trade-off is ~3× less bandwidth than Apple Silicon.
4. Diagnose before you buy. Three layers of misconfiguration (wrong context cap + timeout too short + model cap exceeded) looked like hardware failure. They were entirely config-fixable.
5. The hybrid local/cloud split is the real efficiency lever. Offload the 20% of heavy-context, non-private tasks to Devstral or Gemini 2.5 Pro. Run the other 80% locally where privacy matters.

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Resources & Links

Item	Detail
Qwen3-Coder 30B	Ollama: ollama pull qwen3-coder:30b-262k
NVIDIA DGX Spark	$3,000 — nvidia.com/en-us/project-digits
Mac Studio M3 Ultra	$3,999 (192 GB) / $7,999+ (512 GB) — apple.com
AMD Ryzen AI Max+ 395	Framework Desktop AMD Edition ~$2,000–2,500
ASUS ROG Flow Z13	$2,499 — Ryzen AI Max+ 395, up to 128 GB
AMD MI300X	$25,000+ enterprise — for reference only
Devstral	262K context, free tier — mistral.ai
Gemini 2.5 Pro	1M context — aistudio.google.com
llama.cpp	CPU inference backend — github.com/ggerganov/llama.cpp
Ollama	Local model runtime — ollama.com

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