modelrigs

Yi 1.5 34B on Radeon AI PRO R9700 workstation (32 GB)

34.389B on 32 GB at 644 GB/s. Hardware details · Model details

Yes. Yi 1.5 34B fits on Radeon AI PRO R9700 workstation (32 GB) at the recommended Q4_K_M configuration, requiring approximately 22.5 GB at 4K context. Practical context capacity is 4K.
CompatibilityComfortable
Calculated fit
Yes
Calculated Uses under 80% of usable memory. Room for a long context and other work.
Recommended quantization
Q4_K_M
Calculated gguf
Memory required
22.5 GB
Calculated of 29.4 GB usable — 77%
Max practical context
4K
Calculated model supports 4K

Memory budget at 4K context

Model weights ⓘ
20.0 GB Calculated
KV cache ⓘ
0.9 GB Calculated
Runtime overhead ⓘ
1.6 GB Estimated
Total required
22.5 GB Calculated
Headroom ⓘ
6.9 GB Calculated
Highest-precision quantization that leaves headroom: uses 77% of usable memory at 8K context.
Discrete GPU: 32 GB of VRAM, of which we assume 92% is usable after driver and context overhead.
Performance

Estimated performance · recommended Q4_K_M

Decode, prefill and TTFT below are estimates for Q4_K_M. We do not have a comparable Q4_K_M measurement on this machine.

Estimated decode
—
Estimated Q4_K_M; calculated range
Estimated prefill
—
Estimated Q4_K_M; calculated range
Estimated TTFT at 8K
~5.6–11.6 s
Estimated Q4_K_M; calculated range
Hardware load reference
420 W
Measured machine-level load; not this model run
No throughput figures for this configuration. We do not estimate performance for a model that cannot load.
How the estimate is calculated
  1. Decode: reading 34.4B active parameters at 4.85 bits/weight takes 34.08 ms at 644 GB/s x 95% achieved efficiency.
  2. Prefill: 96 TFLOPS (FP16) x 2.46 calibrated against measured prefill on this platform x 32% assumed model-FLOPs utilisation, divided by 2 x 34.4B parameters per token.
  • 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.

Submit a benchmarkSubmissions stay pending until reviewed.
This model at other quantizations on this machine
QuantizationResidentDiskTotal neededUtilisationFitMax context
Q4_K_Mrecommended20.0 GB20.0 GB22.5 GB77%Comfortable4K
Q5_K_M23.5 GB23.5 GB26.1 GB89%Fits4K
Q8_034.7 GB34.7 GB37.7 GB128%Does not fit0
BF1664.1 GB64.1 GB67.9 GB231%Does not fit0
Cost of running Yi 1.5 34B on this machineUnited States (federal) · C corporation · 8h/day
Monthly economic cost
$34
Calculated after tax
Codex/Claude Code
$100/mo
≈ $100/mo · local is $66 less
Net cash at purchase
$2,521
Calculated VAT not reclaimable
Total over 5 years
$2,044
Calculated after tax, after resale
Cost per USD/1M tokens
—
Calculated

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.

Purchase-price input: $2,521 in United States (federal), tax/VAT excluded · estimated · NL retail (system build) (converted catalogue estimate). This is the localized purchase input; the after-tax economic cost is calculated separately below.

Monthly breakdown

Depreciation
2,143 over 5 years, straight-line to a 378 residual
$35.72
Electricity
21.1 kWh/month at 0.140/kWh
$2.96
Cost of capital
4.0%/yr on 1,450 average capital employed
$4.83
Monthly cost before tax$43.51
Electricity tax shield
Running costs are deductible business expenses
−$0.62
First-year expensing
§179 (100.0%)
−$8.82
Monthly economic cost after tax$34.06

Three different numbers, deliberately

Cash cost
$2,521
Money that leaves the bank account on day one, net of reclaimable VAT.
Accounting depreciation
$35.72/month
$2,143 written down over 5 years to a $378 residual.
After-tax economic cost
$34.06/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
$378 (15%) Assumption
Two GPU generations later, the card is worth a fraction of its list price.
Electricity
$0.140/kWh Assumption
Average power draw
120 W Calculated
Load 420 W for 20% of powered hours, idle 45 W for the rest — a machine that is on is not generating tokens the whole time.
Investment allowances
§179 100.0% Official spec
Worth $529 in total — first-year expensing that replaces later tax depreciation.
Cost of capital
$4.83/month Assumption
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.
Yi 1.5 34B on other hardware
HardwareDecodePriceFit
Nearest alternatives to the Radeon AI PRO R9700 workstation (32 GB)
Yi 1.5 34B on RTX 5090 workstation (1x 32 GB)32 GBYi 1.5 34B on Used RTX 4090 workstation (24 GB)24 GBYi 1.5 34B on RTX 3090 workstation (Ryzen 9 7950X, 64 GB)24 GBYi 1.5 34B on Radeon RX 7900 XTX 24 GB24 GBYi 1.5 34B on Dual used RTX 4090 workstation (48 GB)48 GB
Compare Radeon AI PRO R9700 workstation (32 GB) against RTX 5090 workstation (1x 32 GB) →