A complete reference on recent PCs, GPUs, and specialized chips built for AI inference — with full specs and an AI model recommendation for every device. Last updated: August 2026.
Each category has its own file with full specifications and per-device model recommendations (which models fit in memory, which quantizations to use, and roughly how fast they run):
| File | Covers |
|---|---|
| compatibility-matrix.md | The master lookup: every model × every device, with fit/quantization/speed |
| build-guides.md | Three concrete builds (Budget ~$665–735 / Mid ~$1,465 / High ~$5,300–6,300) for the newest models |
| prices.md | Live price tracker: MSRP vs Aug 2026 street price for every component, with sources & check dates |
| laptop-ai-guide.md | Best-value laptops for local LLMs: M5 Max, Snapdragon X2, Ryzen AI 400 at Aug 2026 prices |
| consumer-gpus.md | RTX 50-series, RTX 4090, AMD RX 9070 XT, Intel Arc |
| ai-desktops.md | DGX Spark, DGX Station, AI mini-PCs, Mac Studio |
| ai-pc-processors.md | Laptop NPUs: Lunar Lake, Strix Point, Snapdragon X/X2, Panther Lake, Ryzen AI 400 |
| apple-silicon.md | M4, M5, M5 Pro/Max/Ultra |
| datacenter-gpus.md | NVIDIA B200/B300/H200, AMD MI355X, Intel Gaudi 3, Crescent Island |
| inference-asics.md | Cerebras, Groq, SambaNova (inference-first chips) |
| hyperscaler-silicon.md | Google TPU v7 Ironwood, AWS Trainium/Inferentia, Microsoft Maia, Meta MTIA |
| edge-embedded.md | Jetson Thor/Orin, Hailo, Google Coral, Raspberry Pi AI Kit |
🌐 Web versions (one page per pick, blog format): the $665 AI machine · the $900 AI laptop · the $1,465 AI tower · the 48 GB AI laptop · the $3,000 24 GB machine · the $6,300 speed monster · the only laptop that runs 120B models · the $4,700 AI supercomputer
Every recommendation in this guide leads with the most recently launched models that fit the hardware, with older generation models listed only as fallbacks. The open-model release cadence in 2026 is relentless (nine open launches in twelve days in July alone), so check the cheat-sheet below and refresh monthly.
The current generation to recommend first, by tier. Older models (Llama 3.3, Qwen2.5, DeepSeek-R1-Distill, Phi-4, SDXL/FLUX) are now second choices.
| Tier | Newest picks | Launched | Notes |
|---|---|---|---|
| Frontier open (API/datacenter) | DeepSeek V4 Flash 0731 | Jul 31, 2026 | Value king: ~79% SWE-bench Verified, 91.6% LiveCodeBench, dirt cheap (MoE, API-first) |
| GLM-5.2 | Jun 13, 2026 | Open agentic leader (~744B total MoE) | |
| Kimi K3 | Jul 27, 2026 (weights) | Moonshot flagship — strong open coding/reasoning, native multimodal | |
| MiniMax M2.5 | Feb 12, 2026 | 230B total / 10B active MoE, open weights, ~$1/hour — “intelligence too cheap to meter” | |
| Mistral Large 3 | Dec 2, 2025 | 675B total / 41B active MoE, Apache 2.0, #2 open on LMArena | |
| Qwen3-Coder-Next | Feb 2026 | Open MoE coder, 256K context | |
| Mid-size local (24–48 GB) | Gemma 3 27B | Jul 2026 | The practical self-host pick — Q4 fits a 24 GB GPU, multimodal |
| Mistral Medium 3 | Jul 2026 | 48GB-class machine, or API | |
| GLM-4.7-Flash | Jan 20, 2026 | 30B-A3B MoE — strongest in the 30B class, 128K context, local-friendly | |
| Devstral 2 / Devstral Small 2 | Dec 10, 2025 | 24B open coding model (beats Qwen3-Coder-30B) | |
| Small / edge (≤14B) | Qwen3.5 sub-12B line | 2026 | The recent practical laptop-class option |
| Gemma 4 sub-12B line | 2026 | Same tier, Google’s latest small models | |
| Ministral 3 (3B/8B/14B) | Dec 2, 2025 | Optimized for RTX PCs, DGX Spark, Jetson; 14B reasoning = 85% AIME 2025 | |
| Vision/multimodal | Gemma 3 27B, Kimi K3 | 2026 | Newest open multimodal; Qwen2.5-VL as workhorse fallback |
| Image generation | Qwen Image 3.0 / 3.0 Pro, Muse Spark 1.2 | Aug 2026 | Open text-to-image, latest releases |
| Speech (ASR) | Voxtral Transcribe 2 | Feb 4, 2026 | On-device STT with real-time diarization — beats Whisper on FLEURS; Whisper as fallback |
| Coding (local) | Devstral 2 / Small 2, GLM-4.7-Flash | Dec 2025–Jan 2026 | Newest open coding models that fit consumer hardware |
| Embeddings/RAG | Qwen3-Embedding, bge-m3 | 2025 | Still the standard for RAG |
| Closed API (no local) | GPT-5.3-Codex, Claude Opus 4.6, Gemini 3 Deep Think | Feb 2026 | Cloud-only; benchmark references |
| NPU tier (chip) | NPU TOPS | Cheapest realistic pick | Street price (Aug 2026) |
|---|---|---|---|
| Snapdragon X2 Elite / Plus (“Glymur”) | 80–85 | Microsoft Surface Laptop 13.8″ (8th Ed); deals to ~$1,080 | ~$1,080–1,400 |
| Ryzen AI 400 (Medusa Point) | 60 | ASUS Zenbook 14 (Ryzen AI 9 465; open-box from ~$714) | ~$750–1,200 |
| Strix Point (Ryzen AI 300, HX 370) | 50 | mid-tier HX 370 ultrabooks | ~$900–1,200 |
| Intel Panther Lake (Core Ultra 300) | ~50 | HP OmniBook X Flip (Walmart listing) | ~$999 |
| Intel Lunar Lake (Core Ultra 200V) | 48 | 2024–25 budget stock (deep discounts) | ~$600 |
| Snapdragon X Elite / X Plus | 45 | budget X Plus stock (Qualcomm “from $599”); X Elite from ~$1,000 | ~$600–1,000 |
| Apple M5 / M4 (Neural Engine) | ~38 | MacBook Air M5 (16-core NE) | ~$1,099 (M4 Air from $999) |
Prices are cheapest street configs (typically 16–32 GB RAM; the RAMpocalypse taxes higher capacities). All tiers run the same newest small-model stack (Ministral 3, Qwen3.5, Gemma 4) — the NPU number mostly decides battery life for always-on AI, not which models fit. Sources & full ranges: prices.md §8.
| Budget | Desktop pick (from build-guides.md) | Laptop pick (from laptop-ai-guide.md) | The honest trade-off | Form factor (battery / noise / power / repairability) |
|---|---|---|---|---|
| ~$700–1,000 | Budget tower — used RTX 3060 12 GB (~$665–735): Ministral 3 14B Q4 @ 40–55 tok/s | Zenbook 14, Ryzen AI 9 465 (~$900–1,150): 8B @ 25–40 tok/s + 60 TOPS NPU | Desktop runs one tier bigger and ~35% faster per dollar; laptop is portable and battery-efficient | Desktop: none / fans audible under load / ~12 W idle, ~275 W load / 9/10 — full ATX: socketed CPU, DIMM slots, PCIe GPU, standard PSU. Laptop: 8–12 h light, ~3 h sustained LLM / quiet / 65 W charger / 4/10 est. — RAM soldered; SSD & battery swappable |
| ~$1,500 | Mid tower — used RTX 5060 Ti 16 GB (~$1,465): Devstral 2 Q4, 27B/30B at Q3 | Vivobook S16 X2 Elite 48 GB (~$1,599): 14B @ 20–30 tok/s, 8B on NPU | Desktop unlocks the 24–30B tier (Q3/Q4); laptop caps at 14B but gives all-day on-device AI | Desktop: none / moderate fan noise / ~400 W / 9/10 — full ATX. Laptop: 15–20 h light, all-day on NPU / near-silent / 65 W charger / 4/10 est. — 48 GB RAM soldered fixed; SSD & battery swappable |
| ~$3,000 | Desktop-only tier — used RTX 4090 24 GB (~$3,000–3,200): Gemma 3 27B + GLM-4.7-Flash at full Q4 | — (no laptop equivalent) | The full-Q4 27–30B experience has no laptop answer in 2026 | Desktop: none / 4090 loud at 450 W (35 dBA) / ~7–33 W idle, ~650 W load / 9/10 — ATX; used card: plan repaste + check the 12VHPWR connector |
| ~$5,200–6,300 | High tower — RTX 5090 32 GB (~$5,300–6,300): 27–30B @ 60–90 tok/s, Mistral Medium 3 ⚠ | M5 Max 128 GB (~$5,200–5,900): 27–30B @ 40–55 tok/s, plus Mistral Medium 3, gpt-oss-120B, 72B vision | The 2026 inversion: the laptop holds bigger models (128 GB unified > 32 GB VRAM); the desktop is faster on what fits (1.79 TB/s vs ~700 GB/s) | Desktop: none / 5090 loud at 575 W (40 dBA) / ~30–46 W idle, ~800 W load / 9/10 — ATX, GPU swappable. Laptop: 15–20 h light, ~3–5 h sustained LLM / silent at idle, fans on under max load / 140 W charger, ~100 W sustained (147 W peak) / 4/10 (iFixit) — RAM & SSD soldered, Apple repairs pricey |
| ~$4,700 (turnkey) | DGX Spark — 128 GB unified, capacity to 120B, slow decode | — | The Spark is the “desktop that holds laptop-class memory” — same 128 GB capacity class as the M5 Max | No battery / quiet SFF (minor coil whine) / ~25–45 W idle, ~150 W load / 4/10 est. — 128 GB RAM soldered on-package; 2242 SSD user-swappable |
Form factor cell format: battery life · noise · power draw · repairability (iFixit-style 0–10, 10 = easiest; “est.” = scored from teardown reports, no official iFixit score). Power draw = system under sustained LLM load (GPU TDP + platform); noise notes assume a mid-tower case with decent fans.
Repairability scores & teardown sources:
Measured at the wall where noted; “GPU” figures are card-only, “system” figures include the rest of the PC. “est.” = no direct measurement found, estimated from comparable hardware/reports.
| Pick | Idle power | Load power | Noise | Sources |
|---|---|---|---|---|
| RTX 3060 12 GB (budget tower) | ~12 W (GPU) | ~175 W GPU · ~275 W system | 0 dBA idle (fans off) · ~28–32 dBA load (est.) | igorslab review |
| RTX 4090 FE (24 GB tower) | ~7–33 W (GPU; display-count dependent) | ~450 W GPU (peak 461 W) · ~650 W system | 35 dBA at full load | TechPowerUp power · TechPowerUp noise · ServeTheHome |
| RTX 5090 FE (high tower) | ~30 W (TPU) · ~46 W (GN desktop) | 575 W TDP · ~600–650 W measured · ~800 W system | 40 dBA at full load | TechPowerUp · GamersNexus · LANOC |
| DGX Spark | ~40–45 W at launch → ~25 W after Feb 2026 update | ~135–145 W from the wall during LLM runs (240 W PSU) | quiet; minor coil whine reported | Tom’s Hardware · ServeTheHome · r/LocalLLaMA |
| M5 Max MacBook Pro 16″ | ~5–10 W (screen on; est.) | ~98 W sustained · ~147 W peak (140 W adapter) | 0 dB idle (fans off) · ~38–45 dBA max under heavy load (est.) | Apple tech specs · power measurements (r/macbook) · Skorppio efficiency test |
Method: hardware price + electricity amortized over 2 h/day of sustained single-stream inference at each pick’s flagship speed (midpoint from compatibility-matrix.md). Desktops amortized over 5 yr, laptops 4 yr (battery/soldered-RAM life); $0.17/kWh US average. Draw = sustained LLM-load figure from the form-factor column.
| Pick | Price | Flagship run (tok/s) | System draw | $/M tokens | $/W | Tokens/kWh |
|---|---|---|---|---|---|---|
| Budget tower · RTX 3060 12 GB | ~$700 | Ministral 3 14B Q4 · 47 | ~275 W | ~$1.4 | $2.5 | ~0.62M |
| Zenbook 14 · Ryzen AI 9 465 | ~$1,025 | Ministral 3 8B · 32 | ~45 W | ~$3.1 | $23 | ~2.6M |
| Mid tower · RTX 5060 Ti 16 GB | ~$1,465 | Devstral 2 Q4 · 52 | ~400 W | ~$2.5 | $3.7 | ~0.47M |
| Vivobook S16 · X2 Elite 48 GB | ~$1,599 | 14B · 25 (8B on NPU · 30) | ~50 W | ~$5.1–6.2 | $32 | ~3.6M (NPU) |
| 24 GB tower · used RTX 4090 | ~$3,100 | Gemma 3 27B Q4 · 47 | ~650 W | ~$5.7 | $4.8 | ~0.26M |
| High tower · RTX 5090 | ~$5,800 | Gemma 3 27B Q4 · 75 | ~800 W | ~$6.5 | $7.3 | ~0.34M |
| M5 Max 128 GB | ~$5,550 | Gemma 3 27B Q4 · 47 | ~100 W | ~$11.3 | $56 | ~1.7M |
| DGX Spark | $4,699 | Gemma 3 27B · 15 | ~150 W | ~$24 | $31 | ~0.36M |
What the numbers say:
Bottom line: under $5K the desktop wins for LLMs per dollar (VRAM > everything — see the fitting math below). At $5K+ the rules invert: the M5 Max laptop and DGX Spark (128 GB unified) beat a 5090 tower on model capacity, while the tower wins on speed and image gen. The 24 GB Q4 sweet spot (~$3,000) is desktop-only.
Same usage envelope as the local table: 338K output tokens/day (2 h/day at the budget tower’s 47 tok/s), 3:1 input:output ratio, cache-miss pricing. Sources: DeepSeek pricing · OpenAI pricing · Anthropic Opus 4.6 announcement. Checked Aug 9, 2026.
| API model | Input $/M | Output $/M | Cost/day | Cost/5 yr | vs budget tower (~$871/5 yr) |
|---|---|---|---|---|---|
| DeepSeek V4 Flash 0731 (value king) | $0.14 | $0.28 | ~$0.24 | ~$430 | API is cheaper than every local build |
| GPT-5.3-Codex (closed) | $1.75 | $14.00 | ~$6.51 | ~$11,900 | local is 13.6× cheaper |
| Claude Opus 4.6 (closed) | $5.00 | $25.00 | ~$13.54 | ~$24,700 | local is 28× cheaper |
| Local pick (5-yr amortized) | DeepSeek V4 Flash (~$430) | GPT-5.3-Codex (~$11,900) | Claude Opus 4.6 (~$24,700) |
|---|---|---|---|
| Budget tower · ~$871 | API ~2× cheaper | local ~14× cheaper | local ~28× cheaper |
| Mid tower · ~$1,713 | API ~4× cheaper | local ~7× cheaper | local ~14× cheaper |
| 24 GB tower · ~$3,503 | API ~8× cheaper | local ~3.4× cheaper | local ~7× cheaper |
| High tower · ~$6,296 | API ~15× cheaper | local ~1.9× cheaper | local ~4× cheaper |
| M5 Max · ~$5,600 | API ~13× cheaper | local ~2.1× cheaper | local ~4.4× cheaper |
| DGX Spark · ~$4,792 | API ~11× cheaper | local ~2.5× cheaper | local ~5.2× cheaper |
Break-even (heavy daily use): GPT-5.3-Codex pays for the budget tower in ~4.5 months and the 5090 tower in ~2.6 years; Claude Opus 4.6 pays for the budget tower in ~2 months and the 5090 tower in ~15 months. DeepSeek V4 Flash never breaks even — at this usage the API is simply cheaper for five straight years.
What this means:
Five plain-English questions — each answer points to the next. Full detail in the build guides and laptop guide.
Q1 Do you need AI on the go (laptop, battery, portability)?
├─ YES ────────────────────────────────────► Q2
└─ NO (it lives on a desk) ────────────────► Q4
Q2 Will you run BIG models (30B+, or up to 70–120B)?
├─ YES ──► M5 Max MacBook Pro 128 GB (~$5,200–5,900) · the only laptop that does
└─ NO ───► Q3
Q3 What matters more: all-day battery + always-on AI, or lowest price?
├─ Battery / on-device AI (voice, Copilot+) ──► Vivobook S16 X2 Elite 48 GB (~$1,599)
└─ Lowest price / best value ──────────────────► Zenbook 14 Ryzen AI 9 465 (~$900–1,150)
Q4 Desktop: what's your budget?
├─ under ~$1,000 ─────► Budget tower · used RTX 3060 12 GB (~$665–735) · newest ≤14B
├─ ~$1,000–1,800 ─────► Mid tower · used RTX 5060 Ti 16 GB (~$1,465) · 27B/30B at Q3
├─ ~$1,800–3,500 ─────► 24 GB tower · used RTX 4090 (~$3,000–3,200) · full Q4
└─ $3,500+ ───────────► Q5
Q5 At $3,500+: max speed on 27–30B, or biggest capacity / zero building?
├─ Speed (plus gaming & image gen) ──► RTX 5090 tower (~$5,300–6,300)
└─ Capacity up to 120B / no building ─► DGX Spark ($4,699, turnkey)
Shortcuts for common cases:
Two rules of thumb dominate “can I run model X?” and “how fast?”:
A Q4_K_M (4-bit) quantized model needs roughly 0.55–0.6 GB per billion parameters, plus ~1–2 GB for context/KV cache and the runtime.
| Model (current examples) | Params | Q4_K_M size | Needs at least |
|---|---|---|---|
| Ministral 3 3B / Qwen3.5 & Gemma 4 sub-12B | 3–12B | 0.7–7 GB | 4–16 GB |
| Ministral 3 14B / Gemma 3 12B | 14B | ~8.6 GB | 12–16 GB |
| Devstral 2 / Gemma 3 27B | 24–27B | 14–16.5 GB | 16–24 GB |
| GLM-4.7-Flash (30B-A3B MoE) | 30B | ~19 GB | 24–32 GB |
| Mistral Medium 3 / gpt-oss-120B | 48–120B | 28–70 GB | 48–128 GB |
| MiniMax M2.5 | 230B | ~130 GB | 128–256 GB |
| Mistral Large 3 / GLM-5.2 | 675–744B | 380–420 GB | 512–748 GB+ |
LLM generation is memory-bandwidth-bound: decode tokens/sec ≈ memory bandwidth ÷ model size (× ~0.7–0.8 efficiency). This is why a 32 GB RTX 5090 (1.79 TB/s) is so much faster than a 128 GB DGX Spark (273 GB/s) on the same small model — and why big-memory machines are great for fitting huge models but generate slowly.
Example ceilings: 8B Q4 on RTX 5090 → ~150+ tok/s; on DGX Spark → ~35–40 tok/s. Gemma 3 27B Q4 on DGX Spark → ~15 tok/s; MiniMax M2.5 Q4 on an M5 Ultra Studio → ~8–10 tok/s (slow but possible).
latest-ai-model-releases-august-2026.html is the place to refresh it.