Qwen3 30B-A3B on GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)
30.532B (3.3B active) on 128 GB at 256 GB/s. Hardware details · Model details
CompatibilityComfortable
Fits
Yes
Calculated Uses under 80% of usable memory. Room for a long context and other work.
Recommended quantization
Q8_0
Calculated gguf
Memory required
33.5 GB
Calculated of 105.0 GB usable — 32%
Max practical context
40K
Calculated model supports 40K
Memory budget at 8K context
Model weights ⓘ
30.8 GB Calculated
KV cache ⓘ
0.8 GB Calculated
Runtime overhead ⓘ
1.9 GB Estimated
Total required
33.5 GB Calculated
Usable memory ⓘ
105.0 GB Assumption
Headroom
71.5 GB Calculated
Highest-precision quantization that leaves headroom: uses 32% of usable memory at 8K context.
Unified LPDDR: the accelerator allocates from the same 128 GB pool as the OS. We assume 82% is available to the inference engine.
Mixture of experts: all 30.532B parameters must be resident in memory even though only ~3.3B are active per token. Memory follows total parameters; speed follows active parameters.
PerformanceEstimated0/10
Decode (generation)
~20.5–29.5 t/s
Estimated calculated, not measured
Prefill (prompt)
~344–714 t/s
Estimated
TTFT at 8K
~11.5–23.9 s
Estimated time to first token
Power while generating
135 W
Measured 0.65 tokens/s per watt
These figures are estimated, not measured. Nobody has published a benchmark of Qwen3 30B-A3B on this machine that we know of, so we calculate throughput from memory bandwidth and active parameter count. Estimates are always shown as a range. Expand the working below to see exactly how the number was produced.
How the estimate is calculated
- Decode: reading 3.3B active parameters at 8.50 bits/weight takes 32.72 ms at 256 GB/s x 42% achieved efficiency.
- MoE routing penalty of 15% applied: expert gathers are less bandwidth-efficient than a dense sweep.
- Prefill: 59 TFLOPS (FP16) x 18% assumed model-FLOPs utilisation, divided by 2 x 10.0B parameters per token.
- Prefill uses 10.0B effective parameters, not the 3.3B active in decode: a batch of hundreds of tokens routes across most of the expert pool.
- MoE decode depends on how well the engine batches expert gathers; real results vary more than for dense models.
- Prefill throughput is highly engine-dependent. Flash attention, batch size and quantized KV all move this number substantially, and for sparse mixture-of-experts models it is the least reliable figure we produce.
- This is a calculated estimate, not a measurement. It assumes a single request, a short prompt, no speculative decoding and a warm model already resident in memory.
Estimator version estimator_v1. Stored with every estimated row so old estimates can be regenerated when the model improves.
This model at other quantizations on this machine
| Quantization | Weights | Total needed | Utilisation | Fit | Max context |
|---|---|---|---|---|---|
MLX 4-bit | 16.6 GB | 18.9 GB | 18% | Comfortable | 40K |
Q4_K_M | 17.8 GB | 20.0 GB | 19% | Comfortable | 40K |
Q8_0recommended | 30.8 GB | 33.5 GB | 32% | Comfortable | 40K |
BF16 | 56.9 GB | 60.3 GB | 57% | Comfortable | 40K |
Cost of running Qwen3 30B-A3B on this machineUnited States (federal) · C corporation · 8h/day
Monthly economic cost
$22
Calculated after tax
Codex/Claude Code
$100/mo
≈ $100/mo · local is $78 less
Net cash at purchase
$1,711
Calculated VAT not reclaimable
Total over 5 years
$1,336
Calculated after tax, after resale
Cost per USD/1M tokens
$7.03
Calculated 3.2M tokens/month
The $100 comparison uses the Codex Pro 5x / Claude Max 5x plans. A fixed planning conversion is used for USD. This compares monthly spend only: subscriptions have usage limits, local hardware has different capabilities and constraints, and taxes or regional pricing may change the charged amount. Prices checked 7 September 2026.
Monthly breakdown
| Depreciation 1,454 over 5 years, straight-line to a 257 residual | $24.23 |
| Electricity 6.7 kWh/month at 0.140/kWh | $0.94 |
| Cost of capital 4.0%/yr on 984 average capital employed | $3.28 |
| Monthly cost before tax | $28.45 |
| Electricity tax shield Running costs are deductible business expenses | −$0.20 |
| First-year expensing §179 (100.0%) | −$5.99 |
| Monthly economic cost after tax | $22.27 |
Three different numbers, deliberately
Cash cost
$1,711
Money that leaves the bank account on day one, net of reclaimable VAT.
Accounting depreciation
$24.23/month
$1,454 written down over 5 years to a $257 residual.
After-tax economic cost
$22.27/month
Depreciation plus running costs plus cost of capital, less the tax those deductions save. This is the figure to compare between machines.
Assumptions and sources (verified 2026-09-07)
VAT rate
0% Official spec
VAT recoverable
0% Official spec
Federal C-corporation estimate at the flat 21% rate. Pass-through entities should use the sole-proprietor estimate as a rougher proxy.
Effective deduction rate
21.00% Calculated
Headline marginal rate 21.00%.
Depreciation
5 years, straight-line Assumption
Residual value
$257 (15%) Assumption
Two GPU generations later, the card is worth a fraction of its list price.
Electricity
$0.140/kWh Assumption
Average power draw
38 W Calculated
Load 135 W for 20% of powered hours, idle 14 W for the rest — a machine that is on is not generating tokens the whole time.
Investment allowances
§179 100.0% Official spec
Worth $359 in total — first-year expensing that replaces later tax depreciation.
Cost of capital
$3.28/month Assumption
- VAT rate: No US federal VAT · verified 2026-09-07
- Marginal tax rate: IRS Publication 542 — Corporations · verified 2026-09-07
- VAT recoverable fraction: site assumption · verified 2026-09-07
- Useful life: IRS Publication 946 — How To Depreciate Property · verified 2026-09-07
- Electricity price: Site assumption · verified 2026-09-07
Assumes the machine generates tokens 20% of its 176 powered hours per month. Cost per token scales inversely with this number — halve the utilisation and the cost per token doubles.
Federal planning estimate only, not tax advice. State and local income tax, sales/use tax and incentives are excluded. It assumes 100% business use, a Section 179 election, enough business income to use it, and that the full annual limit remains available.
Efficiency metrics
Decode per $1,000 spent
14.6 t/s
Calculated purchase price only
Decode per $100/month
112.3 t/s
Calculated after-tax ownership cost
Tokens per joule
0.65
Calculated same as tokens/s per watt
USD per 1M output tokens
$7.03
Calculated at 20% utilisation
Local versus hosted APIs
| Hosted model | USD/1M output | Break-even | API at your volume | Verdict | Comparison type |
|---|---|---|---|---|---|
| Qwen3.8 Flash Alibaba Cloud | $0.42 | 14.5M/mo | $5 | API cheaper | Different model |
| DeepSeek-V4 Flash DeepSeek | $0.66 | 9.2M/mo | $8 | API cheaper | Different model |
| DeepSeek-V4 Pro DeepSeek | $1.98 | 3.1M/mo | $23 | About equal | Different model |
| DeepSeek V4 Pro (Together) Together AI | $4.40 | 1.1M/mo | $67 | Local cheaper | Different model |
| GLM-5.3 Z.ai | $4.40 | 1.4M/mo | $49 | Local cheaper | Different model |
| Claude Haiku 4.5 Anthropic | $5.00 | 1.7M/mo | $41 | Local cheaper | Different model |
Different-model comparison: the hosted model is not the model you would run locally. Treat this as a workload-quality trade-off, not a direct economic equivalence — the frontier model may complete a task in fewer tokens, or complete tasks the local model cannot. Break-even is the monthly output volume at which API spend equals the $22/month economic cost of owning this machine, assuming 8 input tokens per output token. Hosted prices are published in USD. This table is the canonical US default.
Qwen3 30B-A3B on other hardware
| Hardware | Decode | Price | Fit |
|---|---|---|---|
| Quad RTX 5090 workstation (4x 32 GB) | ~301–434 t/s | €13.999 | Comfortable |
| RTX 5090 workstation (1x 32 GB) | 352 t/s | €3.699 | Comfortable |
| Dual RTX 5090 workstation (2x 32 GB) | ~236–339 t/s | €6.499 | Comfortable |
| Used RTX 4090 workstation (24 GB) | ~234–336 t/s | €2.299 | Fits |
| Dual used RTX 4090 workstation (48 GB) | ~232–333 t/s | €4.099 | Comfortable |
| RTX PRO 6000 Blackwell workstation (96 GB) | ~210–303 t/s | €11.499 | Comfortable |
Nearest alternatives to the GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)
Qwen3 30B-A3B on Framework Desktop (Ryzen AI Max+ 395, 128 GB)128 GBQwen3 30B-A3B on NVIDIA DGX Spark 128 GB128 GBQwen3 30B-A3B on CPU-only workstation (Ryzen 9950X, 192 GB DDR5)192 GBQwen3 30B-A3B on Mac Studio M5 Max 128 GB128 GBQwen3 30B-A3B on Radeon AI PRO R9700 workstation (32 GB)32 GB
Compare GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB) against Framework Desktop (Ryzen AI Max+ 395, 128 GB) →
Compare GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB) against Framework Desktop (Ryzen AI Max+ 395, 128 GB) →
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