Qwen3 8B on Quad RTX 5090 workstation (4x 32 GB)

8.191B on 128 GB at 7,168 GB/s. Hardware details · Model details

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Yes. Qwen3 8B fits on Quad RTX 5090 workstation (4x 32 GB) at the recommended Q8_0 configuration, requiring approximately 11.8 GB at 8K context. Practical context capacity is 40K. Expected decode for the recommended configuration is ~196–282 t/s. Estimated
CompatibilityComfortable
Calculated fit
Yes
Calculated Uses under 80% of usable memory. Room for a long context and other work.
Recommended quantization
Q8_0
Calculated gguf
Memory required
11.8 GB
Calculated of 112.6 GB usable — 11%
Max practical context
40K
Calculated model supports 40K

Memory budget at 8K context

Model weights ⓘ
8.3 GB Calculated
KV cache ⓘ
1.1 GB Calculated
Runtime overhead ⓘ
2.4 GB Estimated
Total required
11.8 GB Calculated
Headroom ⓘ
100.8 GB Calculated
Highest-precision quantization that leaves headroom: uses 11% of usable memory at 8K context.
4 GPUs providing 128 GB aggregate VRAM. Assumes tensor- or layer-parallel sharding; each GPU carries its own context and communication buffers.
Multi-GPU throughput depends heavily on interconnect. Without NVLink or NVSwitch, tensor parallelism over PCIe adds latency that partially offsets the extra bandwidth.
Performance

Estimated performance · recommended Q8_0

Decode, prefill and TTFT below are estimates for Q8_0. We do not have a comparable Q8_0 measurement on this machine.

Estimated decode
~196–282 t/s
Estimated Q8_0; calculated range
Estimated prefill
~12230–25400 t/s
Estimated Q8_0; calculated range
Estimated TTFT at 8K
~0.4–0.8 s
Estimated Q8_0; calculated range
Hardware load reference
2250 W
Measured machine-level load; not this model run
How the estimate is calculated
  1. Multi-GPU: 1792 GB/s per card, with each additional card contributing 40% of its bandwidth — 3942 GB/s effective, not the 7168 GB/s aggregate. Cross-GPU collectives use PCIe rather than a dedicated GPU fabric.
  2. Decode: reading 8.2B active parameters at 8.50 bits/weight takes 2.79 ms at 3942 GB/s x 79% achieved efficiency.
  3. Per-token overhead of 1.4 ms (kernel launches, attention bookkeeping, sampling) is significant here — this model is not purely bandwidth-bound on this hardware.
  4. Prefill: 838 TFLOPS (FP16) x 2 for native FP8 tensor cores x 0.71 calibrated against measured prefill on this platform x 26% assumed model-FLOPs utilisation, divided by 2 x 8.2B parameters per token.

  • With few active parameters on fast memory, fixed per-token overhead rather than bandwidth sets the ceiling. Real engines vary widely in how well they hide it.
  • 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_M4.8 GB4.8 GB8.2 GB7%Comfortable40K
Q5_K_M5.6 GB5.6 GB9.1 GB8%Comfortable40K
Q8_0recommended8.3 GB8.3 GB11.8 GB11%Comfortable40K
BF1615.3 GB15.3 GB19.0 GB17%Comfortable40K
Cost of running Qwen3 8B on this machineUnited States (federal) · C corporation · 8h/day
Monthly economic cost
$170
Calculated after tax
Codex/Claude Code
$100/mo
≈ $100/mo · local is $70 more
Net cash at purchase
$12,611
Calculated VAT not reclaimable
Total over 5 years
$10,224
Calculated after tax, after resale
Cost per USD/1M tokens
$5.63
Calculated 30.3M 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.

Purchase-price input: $12,611 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
10,719 over 5 years, straight-line to a 1,892 residual
$178.65
Electricity
106.0 kWh/month at 0.140/kWh
$14.83
Cost of capital
4.0%/yr on 7,251 average capital employed
$24.17
Monthly cost before tax$217.65
Electricity tax shield
Running costs are deductible business expenses
−$3.11
First-year expensing
§179 (100.0%)
−$44.14
Monthly economic cost after tax$170.40

Three different numbers, deliberately

Cash cost
$12,611
Money that leaves the bank account on day one, net of reclaimable VAT.
Accounting depreciation
$178.65/month
$10,719 written down over 5 years to a $1,892 residual.
After-tax economic cost
$170.40/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
$1,892 (15%) Assumption
Two GPU generations later, the card is worth a fraction of its list price.
Electricity
$0.140/kWh Assumption
Average power draw
602 W Calculated
Load 2250 W for 20% of powered hours, idle 190 W for the rest — a machine that is on is not generating tokens the whole time.
Investment allowances
§179 100.0% Official spec
Worth $2,648 in total — first-year expensing that replaces later tax depreciation.
Cost of capital
$24.17/month Assumption
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
18.9 t/s
Calculated purchase price only
Decode per $100/month
140.1 t/s
Calculated after-tax ownership cost
Tokens per joule
0.40
Calculated same as tokens/s per watt
USD per 1M output tokens
$5.63
Calculated at 20% utilisation
Local versus hosted APIs
Hosted modelUSD/1M outputBreak-evenAPI at your volumeVerdictComparison type
Qwen3.8 Flash
Alibaba Cloud
$0.42110.7M/mo$47API cheaperDifferent model
DeepSeek-V4.1 Flash
DeepSeek
$0.6094.7M/mo$54API cheaperDifferent model
DeepSeek-V4 Pro
DeepSeek
$1.9823.5M/mo$220Local cheaperDifferent model
DeepSeek V4 Pro (Together)
Together AI
$4.408.0M/mo$641Local cheaperDifferent model
GLM-5.3
Z.ai
$4.4010.9M/mo$472Local cheaperDifferent model
Claude Haiku 4.5
Anthropic
$5.0013.1M/mo$393Local cheaperDifferent 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 $170/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.