Author: IAIP Research Team
Date: April 15, 2026
Audience: Guillaume Descoteaux-Isabelle
Purpose: Purchase-decision guide for Mac Mini configurations running local AI models via Ollama/OpenClaw
Revision: v2.0 — incorporates pricing verification and reviewer corrections
For small models (7B–14B), buy a Mac Mini M4 with 24 GB RAM and 512 GB SSD at $999 USD (~$1,379 CAD). This runs Llama 3.1 8B, Mistral 7B, CodeLlama 7B, and DeepSeek-Coder 6.7B at 28–35 tok/s (Metal backend) or potentially faster with the MLX preview backend. It is a capable, quiet, low-power local inference node for a developer who already has cloud subscriptions.
For large models (~40 GB, including 70B-class), buy a Mac Mini M4 Pro (12-core) with 64 GB RAM and 1 TB SSD at $2,199 USD (~$3,035 CAD). This is the only Mac Mini configuration that can load Llama 3.1 70B Q4 alongside a full development environment. Expect 6–8 tok/s for 70B — usable for single-user interactive chat but not fast. Models up to 33B–34B run comfortably at 10–14 tok/s.
RAM is soldered and cannot be upgraded. Buy more than you think you need. The $1,200 gap between Scenario A and B buys 10× the model-size capability.
Mac Studio M4 Max (128 GB) at $3,699 USD is the next step if Mac Mini M4 Pro proves insufficient — it doubles memory bandwidth (546 GB/s vs 273 GB/s) and delivers 14–20 tok/s on 70B Q4.
Use a hybrid strategy: Route latency-tolerant and privacy-sensitive queries to local Ollama; route complex reasoning and long-context tasks to cloud (GitHub Copilot with GPT-4.1/GPT-5.x, OpenAI Codex). This maximizes both cost-efficiency and capability.
Traditional PC setups separate CPU RAM from GPU VRAM. An NVIDIA RTX 4090 has 24 GB VRAM — a 70B Q4 model requiring ~38 GB of VRAM simply will not fit without a multi-GPU configuration costing $5,000–$10,000+.
Apple Silicon uses a unified memory architecture (UMA):
LLM token generation is memory-bandwidth-bound, not compute-bound. Each output token requires reading the model's entire weight tensor from memory. The governing equation:
Theoretical max tok/s ≈ Memory Bandwidth (GB/s) ÷ Model Size in Memory (GB)
| Chip | Bandwidth | 7B Q4 (~4 GB) | 13B Q4 (~8 GB) | 34B Q4 (~19 GB) | 70B Q4 (~38 GB) |
|---|---|---|---|---|---|
| M4 | 120 GB/s | ~30 tok/s | ~15 tok/s | ~6 tok/s | N/A (RAM limit) |
| M4 Pro | 273 GB/s | ~68 tok/s | ~34 tok/s | ~14 tok/s | ~7 tok/s |
| M4 Max | 546 GB/s | ~136 tok/s | ~68 tok/s | ~29 tok/s | ~14 tok/s |
Real-world performance is typically 60–80% of theoretical due to attention computation, KV-cache management, and OS overhead. The M4 Max is only available in the Mac Studio ($3,699+) or MacBook Pro — not the Mac Mini.
Sources: Apple Silicon specs (apple.com); bandwidth-throughput formula per llama.cpp community (GitHub ggml-org/llama.cpp#4167).
| Spec | M4 (Base) | M4 Pro (12-core CPU) | M4 Pro (14-core CPU) |
|---|---|---|---|
| CPU Cores | 10 (4P + 6E) | 12 (8P + 4E) | 14 (10P + 4E) |
| GPU Cores | 10 | 16 | 20 |
| Neural Engine | 16-core, 38 TOPS | 16-core, 38 TOPS | 16-core, 38 TOPS |
| Memory Bandwidth | 120 GB/s | 273 GB/s | 273 GB/s |
| Max Unified Memory | 32 GB | 64 GB | 64 GB |
| Max Storage | 2 TB | 8 TB | 8 TB |
| Thunderbolt | TB4 (×3) | TB5 (×3) | TB5 (×3) |
| Ethernet | Gigabit (10 GbE opt.) | 10 Gigabit | 10 Gigabit |
Source: Apple Mac Mini specs page (apple.com/mac-mini/specs).
| RAM | Storage | Price (USD) | Price (CAD, est.) |
|---|---|---|---|
| 16 GB | 256 GB | $599 | ~$827 |
| 16 GB | 512 GB | $799 | ~$1,103 |
| 24 GB | 512 GB | $999 | ~$1,379 |
| 32 GB | 512 GB | ~$1,199 (CTO) | ~$1,655 |
CTO upgrade costs: +$200 for 16→24 GB RAM, +$400 for 16→32 GB RAM; +$200 per SSD tier.
| RAM | Storage | Price (USD) | Price (CAD, est.) |
|---|---|---|---|
| 24 GB | 512 GB | $1,399 | ~$1,931 |
| 24 GB | 1 TB | $1,599 | ~$2,207 |
| 48 GB | 512 GB | $1,799 | ~$2,483 |
| 48 GB | 1 TB | $1,999 | ~$2,759 |
| 64 GB | 1 TB | $2,199 | ~$3,035 |
| 64 GB | 2 TB | $2,599 | ~$3,587 |
| RAM | Storage | Price (USD) | Price (CAD, est.) |
|---|---|---|---|
| 24 GB | 1 TB | $1,799 | ~$2,483 |
| 48 GB | 1 TB | $2,199 | ~$3,035 |
| 64 GB | 1 TB | $2,399–$2,499 | ~$3,311–$3,449 |
⚠️ Pricing note: The 14-core/20-core M4 Pro upgrade adds $200–$300 over the 12-core base at equivalent RAM/storage. Prices verified against Apple Store, B&H Photo, and Klarna aggregator listings (April 2026). The 12-core variant at $2,199 offers the best value for inference workloads since memory bandwidth (273 GB/s) is identical between the 12-core and 14-core M4 Pro — additional GPU cores provide marginal benefit for LLM inference.
| Upgrade | Cost |
|---|---|
| 16→24 GB RAM (base M4) | +$200 |
| 16→32 GB RAM (base M4) | +$400 |
| 24→48 GB RAM (M4 Pro) | +$400 |
| 24→64 GB RAM (M4 Pro) | +$600 |
| 512 GB→1 TB SSD | +$200 |
| 1→2 TB SSD | +$400 |
| 10 GbE Ethernet (base M4) | +$100 |
🔒 RAM is soldered and cannot be upgraded after purchase. This is the single most important decision. Choose more than you think you need today.
Sources: Apple Store (apple.com/shop), B&H Photo (bhphotovideo.com), MacPrices.net, AppleInsider. CAD conversion at 1 USD ≈ 1.38 CAD (April 2026 rate per valutafx.com).
| Model | Parameters | Quantization | Model RAM | + KV Cache (4K ctx) | Total RAM Needed | Disk Space |
|---|---|---|---|---|---|---|
| Phi-3 Mini | 3.8B | Q4 | ~2.3 GB | ~0.4 GB | ~3 GB | 2.3 GB |
| Phi-4 | 14B | Q4KM | ~8 GB | ~0.8 GB | ~9 GB | 8 GB |
| DeepSeek-Coder 6.7B | 6.7B | Q4KM | ~3.8 GB | ~0.5 GB | ~4 GB | 3.8 GB |
| Mistral 7B | 7B | Q4KM | ~4.1 GB | ~0.5 GB | ~5 GB | 4.1 GB |
| CodeLlama 7B | 7B | Q4KM | ~4.0 GB | ~0.5 GB | ~5 GB | 4.0 GB |
| Llama 3.1 8B | 8B | Q4KM | ~4.5 GB | ~0.5 GB | ~5 GB | 4.6 GB |
| Llama 3.1 8B | 8B | Q8_0 | ~8.5 GB | ~0.5 GB | ~9 GB | 8.5 GB |
| CodeLlama 13B | 13B | Q4KM | ~7.9 GB | ~0.8 GB | ~9 GB | 7.9 GB |
| DeepSeek-R1 32B | 32B | Q4KM | ~18 GB | ~1.2 GB | ~19 GB | 18 GB |
| DeepSeek-Coder 33B | 33B | Q4KM | ~19 GB | ~1.2 GB | ~20 GB | 19 GB |
| Mixtral 8×7B (MoE) | 46.7B eff. | Q4KM | ~18 GB | ~1.5 GB | ~20 GB | 26 GB |
| Qwen2.5 72B | 72B | Q4KM | ~39 GB | ~2.5 GB | ~42 GB | 40 GB |
| Llama 3.1 70B | 70B | Q4KM | ~38 GB | ~2.5 GB | ~41 GB | 38 GB |
| Llama 3.1 70B | 70B | Q8_0 | ~72 GB | ~2.5 GB | ~75 GB | 72 GB |
Notes: - KV cache grows with context length. At 32K context, a 70B model's KV cache can reach ~10–20 GB, making 64 GB very tight. - "Total RAM Needed" is the model-only minimum — add 8–14 GB for macOS + dev tools (see below) for real-world planning. - Qwen2.5 72B Q4 fits on 64 GB but leaves only ~10 GB for OS + tools — tight. A 48 GB config cannot load it.
| Component | Typical RAM | Heavy Usage |
|---|---|---|
| macOS system | 3–5 GB | 5–7 GB |
| VS Code / Cursor | 1–2 GB | 3–5 GB |
| Docker Desktop | 2–4 GB | 8–16 GB |
| Browser (10–20 tabs) | 1–4 GB | 5–8 GB |
| OpenClaw agent process | 0.5–1 GB | 1–2 GB |
| Terminal / misc | 0.5–1 GB | 1–2 GB |
| Total dev overhead | 8–14 GB | 16–30 GB |
| Mac Mini Config | Total RAM | OS + Dev Tools | Available for Models |
|---|---|---|---|
| M4, 16 GB | 16 GB | ~10 GB | ~6 GB — 7B Q4 only, barely |
| M4, 24 GB | 24 GB | ~10 GB | ~14 GB — up to 13B Q4 |
| M4, 32 GB | 32 GB | ~10 GB | ~22 GB — up to 33B Q4 tight |
| M4 Pro, 24 GB | 24 GB | ~10 GB | ~14 GB — up to 13B Q4 |
| M4 Pro, 48 GB | 48 GB | ~12 GB | ~36 GB — 33B comfortable; 70B won't fit |
| M4 Pro, 64 GB | 64 GB | ~12 GB | ~52 GB — 70B Q4 with room to spare |
Sources: Ollama documentation, llama.cpp community calculations, macOS Activity Monitor user reports (Reddit r/LocalLLaMA, Hacker News).
Mac Mini M4, 24 GB RAM, 512 GB SSD — $999 USD (~$1,379 CAD)
| Spec | Detail |
|---|---|
| Chip | M4, 10-core CPU, 10-core GPU |
| RAM | 24 GB unified memory |
| Storage | 512 GB SSD (upgrade to 1 TB at ~$1,199 recommended for model library) |
| Bandwidth | 120 GB/s |
| Price | $999 USD |
All benchmarks below use the default Metal backend (llama.cpp). See MLX note below for potential speedups.
| Model | Disk Space | RAM Used | tok/s (Metal) | Verdict |
|---|---|---|---|---|
| Llama 3.1 8B Q4KM | 4.6 GB | ~5 GB | 28–35 | ✅ Excellent |
| Mistral 7B Q4KM | 4.1 GB | ~5 GB | 28–35 | ✅ Excellent |
| CodeLlama 7B Q4KM | 4.0 GB | ~5 GB | 28–35 | ✅ Excellent |
| Phi-3 Mini Q4 | 2.3 GB | ~3 GB | 40–50 | ✅ Very fast |
| DeepSeek-Coder 6.7B Q4 | 3.8 GB | ~4 GB | 30–38 | ✅ Excellent |
| Qwen 2.5 7B Q4 | 4.0 GB | ~5 GB | 28–35 | ✅ Excellent |
| Llama 3.1 8B Q8_0 (higher quality) | 8.5 GB | ~9 GB | 18–22 | ✅ Good |
| CodeLlama 13B Q4KM | 7.9 GB | ~9 GB | 8–12 | ⚠️ Usable, slower |
MLX Backend (Preview — Ollama 0.19+): As of April 2026, the MLX backend is available in preview and must be enabled manually with OLLAMA_MLX=1. On models ≤8B it delivers approximately 21–29% faster token generation vs the Metal backend. However, MLX is reported to require ≥32 GB RAM for optimal operation — the 24 GB Scenario A configuration may not fully benefit. Performance gains on 24 GB systems are unconfirmed. (Sources: Ollama blog March 2026; dev.to/alanwest; byteiota.com)
| Model | Why Not |
|---|---|
| Mixtral 8×7B Q4KM (~20 GB loaded) | Exceeds available RAM after OS/dev tools (~14 GB free) |
| DeepSeek-Coder 33B Q4 (~20 GB) | Same — needs ≥48 GB total |
| Llama 3.1 70B Q4 (~41 GB) | Far exceeds available RAM |
| Qwen2.5 72B Q4 (~42 GB) | Far exceeds available RAM |
| Any model >13B parameters | Insufficient headroom for model + KV cache + tools |
This configuration is ideal when:
Budget alternative: Mac Mini M4, 16 GB, 256 GB at $599 can technically run 7B Q4 models, but with only ~6 GB free RAM after OS, it is too tight for a developer who also runs Docker and browsers. Not recommended.
Mac Mini M4 Pro (12-core CPU / 16-core GPU), 64 GB RAM, 1 TB SSD — $2,199 USD (~$3,035 CAD)
| Spec | Detail |
|---|---|
| Chip | M4 Pro, 12-core CPU, 16-core GPU |
| RAM | 64 GB unified memory |
| Storage | 1 TB SSD (2 TB for large model library: $2,599 USD) |
| Bandwidth | 273 GB/s |
| Price | $2,199 USD |
Why 12-core, not 14-core? The 14-core/20-core M4 Pro with 64 GB/1 TB costs $2,399–$2,499 — a $200–$300 premium for 4 additional GPU cores. Memory bandwidth is identical (273 GB/s), and LLM inference is bandwidth-bound, not GPU-core-bound. The extra GPU cores help for graphics and Metal compute workloads but provide negligible benefit for token generation. Save the $200–$300.
A 48 GB configuration ($1,999) cannot load Llama 3.1 70B Q4: - Model + KV cache: ~41 GB - macOS + dev tools: ~12 GB - Total needed: ~53 GB > 48 GB
48 GB is viable for 33B-class models (DeepSeek-Coder 33B, CodeLlama 34B, Mixtral 8×7B — all ~20 GB loaded). If you know you will never need 70B models, 48 GB at $1,999 saves $200. But given that RAM cannot be upgraded, 64 GB is the safer choice for future-proofing.
All benchmarks use the Metal backend unless noted. MLX preview may improve speeds by ~25% for 8B models when enabled (OLLAMA_MLX=1); MLX performance on 64 GB systems with large models is not yet widely validated.
| Model | Disk Space | RAM Used | tok/s | Verdict |
|---|---|---|---|---|
| Llama 3.1 70B Q4KM | 38 GB | ~41 GB | 6–8 | ✅ Usable for single-user chat |
| Qwen2.5 72B Q4KM | 40 GB | ~42 GB | 5–7 | ✅ Usable but tight on RAM |
| DeepSeek-R1 32B Q4KM | 18 GB | ~19 GB | 11–14 | ✅ Good for reasoning tasks |
| DeepSeek-Coder 33B Q4KM | 19 GB | ~20 GB | 10–14 | ✅ Good for coding |
| Mixtral 8×7B Q4KM | 26 GB | ~20 GB | 12–18 | ✅ Good, MoE efficiency |
| CodeLlama 34B Q4KM | 19 GB | ~20 GB | 10–14 | ✅ Good for coding |
| Llama 3.1 8B Q4KM | 4.6 GB | ~5 GB | 35–45 | ✅ Blazing fast |
| Multiple 7B models concurrent | ~10 GB | ~12 GB | 25–35 each | ✅ Multi-model supported |
| Llama 3.1 70B Q8_0 | 72 GB | ~75 GB | — | ❌ Exceeds 64 GB |
Sources: like2byte.com (DeepSeek-R1 benchmarks); compute-market.com; Reddit r/LocalLLaMA user reports; Ollama community benchmarks.
| Model Class | tok/s on M4 Pro 64 GB | Feel |
|---|---|---|
| 7B–8B Q4 | 35–45 | Instant — faster than you can read |
| 13B Q4 | 14–18 | Very responsive |
| 32B–34B Q4 | 10–14 | Comfortable interactive use |
| 70B Q4 | 6–8 | Noticeably slower; usable for chat, not for rapid iteration |
Prompt processing (prefill) is much faster than token generation: ~326 tok/s for 8B models on M4. A 1,000-token prompt is ingested in ~3 seconds.
Concurrency: Ollama supports OLLAMA_NUM_PARALLEL for simultaneous requests. On 64 GB with a 70B model loaded, there is no room for a second large model. For multi-agent OpenClaw setups that route to Ollama, plan to use one large model at a time or multiple 7B models concurrently.
If M4 Pro 64 GB proves insufficient, the upgrade path is:
Mac Studio M4 Max, 128 GB RAM, 1 TB SSD — $3,699 USD (~$5,105 CAD)
| Factor | Mac Mini M4 Pro 64 GB | Mac Studio M4 Max 128 GB |
|---|---|---|
| Bandwidth | 273 GB/s | 546 GB/s (2×) |
| 70B Q4 tok/s | 6–8 | 14–20 (2–3× faster) |
| Can load 70B Q4? | ✅ Yes (tight) | ✅ Yes, with massive headroom |
| 70B at 32K context? | ⚠️ Very tight — KV cache 10–20 GB | ✅ Comfortable |
| Run 2 large models? | ❌ No room | ✅ Yes |
| 70B Q8 (higher quality)? | ❌ Exceeds 64 GB | ✅ ~75 GB fits in 128 GB |
| Price | $2,199 | $3,699 |
| Power draw | 50–80 W | ~75–110 W |
| Form factor | 5×5 in. | Larger desktop unit |
Verdict: The Mac Studio makes sense if you routinely need 70B at long context (32K+), want multiple large models loaded, or need faster 70B generation for productive interactive use. For occasional 70B queries alongside 7B–33B daily drivers, the Mac Mini M4 Pro is sufficient and saves $1,500.
Sources: Apple Mac Studio pricing (microcenter.com, bhphotovideo.com — verified $3,699 for 128 GB/1 TB); Macworld Mac Studio M4 Max review.
| Scenario A (Minimal) | Scenario B (Evolved) | |
|---|---|---|
| Machine | Mac Mini M4 | Mac Mini M4 Pro (12-core) |
| RAM | 24 GB | 64 GB |
| Storage | 512 GB (1 TB recommended) | 1 TB (2 TB recommended) |
| Bandwidth | 120 GB/s | 273 GB/s (2.3×) |
| Price (USD) | $999 | $2,199 |
| Price (CAD, est.) | ~$1,379 | ~$3,035 |
| Max model size | 13B Q4 (with tools running) | 70B Q4 (with tools running) |
| Sweet spot models | 7B–8B Q4 | 32B–34B Q4 |
| 7B Q4 tok/s | 28–35 | 35–45 |
| 70B Q4 tok/s | N/A | 6–8 |
| Multi-model concurrency | 2–3 small (7B) models | 3–4 small or 1 large + 1 small |
| Power draw (inference) | ~45 W | ~65–80 W |
| Primary use | Supplement cloud AI | Self-sufficient local AI |
| Upgrade path | → Scenario B ($2,199) | → Mac Studio M4 Max ($3,699) |
The Mac Mini's compact 5×5-inch enclosure has real thermal limits under sustained AI inference loads.
| Workload Type | Temperature Range | Time to Throttle | Performance Impact |
|---|---|---|---|
| Interactive chat (bursty queries) | 60–80°C | No throttling | ✅ Full performance |
| CPU-heavy batch work (compile, Docker) | 68–74°C | No throttling | <3% drop |
| Sustained LLM inference (100% GPU/CPU) | 95–105°C (junction) | 8–10 minutes | 30–45% drop |
| Sustained inference + external fan | 85–95°C | ~25 minutes | 10–20% less throttling |
Note on "118°C" reports: Some community benchmarks report GPU thermal-junction readings up to 118°C under extreme continuous load. This is the junction temperature (hotspot on the die), not the surface or package temperature. While high, Apple Silicon is rated for junction temps up to 110–120°C. Still, sustained operation at these temperatures triggers aggressive thermal throttling.
For interactive Ollama use (Scenario A & B typical): Queries are bursty — a few seconds of inference followed by idle time. The Mac Mini cools between queries. Thermal throttling is not a practical concern for interactive chat or coding-assistant workflows.
For sustained batch inference (processing documents, running multi-agent loops): Stock cooling throttles within 10 minutes, settling at 55–70% of peak performance. Mitigations:
sudo powermetrics or third-party tools reduces temps but increases noise.Sources: VPSMac 72-hour stress test (vpsmac.com); Apple Community Forums (discussions.apple.com/thread/255854367); blog.shiptasks.com thermal analysis.
| Model Collection | Disk Space |
|---|---|
| 3–4 small models (7B Q4 variants) | ~15–20 GB |
| 5–8 mixed models (7B + 13B) | ~40–60 GB |
| Full Scenario B library (7B + 33B + 70B) | ~80–120 GB |
| Large library with multiple quantizations | ~150–200 GB |
Ollama stores models in ~/.ollama/models/. Models can be deleted and re-downloaded at any time (ollama rm <model> / ollama pull <model>).
Guillaume already has OpenAI Codex and GitHub Copilot subscriptions. The optimal strategy is not "local vs cloud" — it is routing queries to the right backend based on the task.
| Scenario | Why Local |
|---|---|
| Privacy-sensitive queries (proprietary code, client data) | Data never leaves your machine |
| Offline work (travel, network outages) | No internet required |
| High-frequency small queries (autocomplete, quick Q&A) | Zero latency, no rate limits |
| Experimentation (testing prompts, comparing models) | No API cost, no quota consumption |
| Always-on background agent (file watcher, code review daemon) | No per-request cost |
| Scenario | Why Cloud |
|---|---|
| Complex reasoning (architecture decisions, long analysis) | GPT-4.1, GPT-5.x, Claude Opus are far more capable than local 7B–70B |
| Long context (entire codebases, large documents) | Cloud models handle 128K+ context; local 70B struggles past 8K on 64 GB |
| Speed on large models | GPT-4.1 responds in 1–3 seconds; local 70B takes 5–10 seconds per response |
| Multi-modal tasks (image understanding, code from screenshots) | Not available locally |
| Latest model capabilities | GPT-5.4, Claude Opus 4.6 — no local equivalent |
Configure OpenClaw's multi-provider routing to automatically select the right backend:
{
"agents": {
"defaults": {
"model": {
"primary": "github-copilot/gpt-4.1", // Cloud default (unlimited for paid users)
"fallbacks": ["ollama/llama3.1:8b"] // Local fallback
}
}
}
}
For privacy-sensitive tasks, explicitly route to local:
# In OpenClaw, select the local model for a specific task
ollama/deepseek-coder:6.7b
Guillaume's GitHub Copilot subscription includes access to (at no additional per-request cost for most models):
| Model | Status | Best For |
|---|---|---|
| GPT-4.1 | ✅ Current default (unlimited) | General coding, reasoning |
| GPT-5.2 | ✅ Available | Advanced reasoning |
| GPT-5.3-Codex | ✅ Available | Code-optimized tasks |
| GPT-5.4 | ✅ Available | Latest flagship |
| Claude Sonnet 4.6 | ✅ Available | Nuanced analysis |
| Claude Opus 4.6 | ✅ Available (premium quota) | Deep reasoning |
| Claude Haiku 4.5 | ✅ Available | Fast, lightweight |
⚠️ Premium request quotas: Some models (Claude Opus, o1, GPT-5.4) may consume limited monthly premium request allocations depending on plan tier. GPT-4.1 is unlimited for paid Copilot users. (Source: github.blog changelog; GitHub community discussions)
⚠️ GPT-4o is deprecated in GitHub Copilot. If existing configurations reference
gpt-4o, update them togpt-4.1.
The Mac Mini M5 refresh is expected around WWDC in June 2026 — approximately 2 months from this document's date. Early M5 Max benchmarks suggest:
Should Guillaume wait?
Sources: Macworld (macworld.com/article/2964754); TechRepublic; zeerawireless.com.
Given that Guillaume already has OpenAI Codex and GitHub Copilot subscriptions for cloud inference, local AI is a supplement, not a replacement. The purchase decision depends on how he intends to use local models:
→ Buy: Mac Mini M4, 24 GB RAM, 512 GB SSD — $999 USD (~$1,379 CAD)
This configuration runs Llama 3.1 8B, Mistral 7B, CodeLlama 7B, and DeepSeek-Coder 6.7B at 28–35 tok/s — fast enough for real-time coding assistance. Cloud handles the heavy lifting. Total annual cost: ~$999 hardware + existing subscriptions.
→ Buy: Mac Mini M4 Pro (12-core), 64 GB RAM, 1 TB SSD — $2,199 USD (~$3,035 CAD)
This is the configuration that opens the door to large local models: DeepSeek-Coder 33B (10–14 tok/s), DeepSeek-R1 32B (11–14 tok/s), Mixtral 8×7B (12–18 tok/s), and Llama 3.1 70B (6–8 tok/s). The $1,200 premium over Scenario A buys 10× the model-size capability and 2.3× the memory bandwidth.
Go with Scenario B ($2,199 USD). Here's why:
If the June 2026 M5 timeline works with Guillaume's schedule, waiting 2 months could yield a meaningful performance uplift at the same price point. But the M4 Pro 64 GB is a strong purchase today.
Document produced April 15, 2026. All prices are USD MSRP unless otherwise noted. CAD estimates use 1 USD ≈ 1.38 CAD. Benchmark numbers are from community reports and may vary ±15% based on context length, quantization variant, system load, and Ollama version. RAM is soldered on all Apple Silicon Macs and cannot be upgraded after purchase.