Compatible devices for Qwen2.5 14B
Every hardware profile in our catalog graded for Qwen2.5 14B, best fit first. For sellable vendor configurations, see the device catalog.
- Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~30GB) with ~533.2GB headroom — about 18 concurrent instances.
FP16 · ~30GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~30GB) with ~533.2GB headroom — about 18 concurrent instances.
FP16 · ~30GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~30GB) with ~139GB headroom — about 5 concurrent instances.
FP16 · ~30GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~30GB) with ~94.1GB headroom — about 4 concurrent instances.
FP16 · ~30GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~30GB) with ~94.1GB headroom — about 4 concurrent instances.
FP16 · ~30GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~30GB) with ~40.4GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~30GB) with ~40.4GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - HP Z8 Fury G5 WorkstationHP · AI Workstations
Fits at FP16 (~30GB) with ~54.5GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - Lenovo ThinkStation PX WorkstationLenovo · AI Workstations
Fits at FP16 (~30GB) with ~54.5GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - Supermicro AI WorkstationSupermicro · AI Workstations
Fits at FP16 (~30GB) with ~54.5GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - Apple Mac Studio (M2 Ultra)Apple · Apple Silicon
Fits at FP16 (~30GB) with ~104.4GB headroom — about 4 concurrent instances.
FP16 · ~30GBRuns well - Quad RTX 4090 AI Workstation (reference profile)Reference · AI Workstations
Fits at FP16 (~30GB) with ~54.5GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - Dell Precision 7960 AI WorkstationDell · AI Workstations
Fits at FP16 (~30GB) with ~12.2GB headroom — about 1 concurrent instance.
FP16 · ~30GBRuns well - NVIDIA A100 80GBNVIDIA · Datacenter GPUs
Fits at FP16 (~30GB) with ~40.4GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - Cloud A100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~30GB) with ~40.4GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - Apple Mac Studio (M4 Max)Apple · Apple Silicon
Fits at FP16 (~30GB) with ~59.6GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - Coding Agent Workstation (reference profile)Reference · AI Workstations
Fits at FP16 (~30GB) with ~12.2GB headroom — about 1 concurrent instance.
FP16 · ~30GBRuns well - NVIDIA L40SNVIDIA · Datacenter GPUs
Fits at FP16 (~30GB) with ~12.2GB headroom — about 1 concurrent instance.
FP16 · ~30GBRuns well - Cloud L40S 48GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~30GB) with ~12.2GB headroom — about 1 concurrent instance.
FP16 · ~30GBRuns well - Law Firm Private AI Box (reference profile)Reference · AI Appliances
Fits at FP16 (~30GB) with ~12.2GB headroom — about 1 concurrent instance.
FP16 · ~30GBRuns well - NVIDIA RTX 6000 Ada GenerationNVIDIA · Professional GPUs
Fits at FP16 (~30GB) with ~12.2GB headroom — about 1 concurrent instance.
FP16 · ~30GBRuns well - AMD Radeon PRO W7900AMD · Professional GPUs
Fits at FP16 (~30GB) with ~12.2GB headroom — about 1 concurrent instance.
FP16 · ~30GBRuns well - NVIDIA RTX A6000NVIDIA · Professional GPUs
Fits at FP16 (~30GB) with ~12.2GB headroom — about 1 concurrent instance.
FP16 · ~30GBRuns well - Accounting / Odoo AI Box (reference profile)Reference · AI Appliances
Fits at Q8_0 (~16GB) with ~5.1GB headroom — about 1 concurrent instance.
Q8_0 · ~16GBRuns well - NVIDIA GeForce RTX 4090NVIDIA · Consumer GPUs
Fits at Q8_0 (~16GB) with ~5.1GB headroom — about 1 concurrent instance.
Q8_0 · ~16GBRuns well - AMD Radeon RX 7900 XTXAMD · Consumer GPUs
Fits at Q8_0 (~16GB) with ~5.1GB headroom — about 1 concurrent instance.
Q8_0 · ~16GBRuns well - NVIDIA GeForce RTX 3090NVIDIA · Consumer GPUs
Fits at Q8_0 (~16GB) with ~5.1GB headroom — about 1 concurrent instance.
Q8_0 · ~16GBRuns well - Local Office AI Appliance (reference profile)Reference · AI Appliances
Fits at Q4_K_M (~10GB) with ~4.1GB headroom — about 1 concurrent instance.
Q4_K_M · ~10GBRuns well - Hotel AI Automation Box (reference profile)Reference · AI Appliances
Fits at Q4_K_M (~10GB) with ~4.1GB headroom — about 1 concurrent instance.
Q4_K_M · ~10GBRuns well - Intel Arc A770 16GBIntel · Consumer GPUs
Fits at Q4_K_M (~10GB) with ~4.1GB headroom — about 1 concurrent instance.
Q4_K_M · ~10GBRuns well - NVIDIA DGX Spark (GB10)NVIDIA · AI Appliances
Fits at FP16 (~30GB) but limited bandwidth makes token generation slow for a 14B model.
FP16 · ~30GBRuns slowly - ASUS Ascent GX10 (GB10)ASUS · AI Appliances
Fits at FP16 (~30GB) but limited bandwidth makes token generation slow for a 14B model.
FP16 · ~30GBRuns slowly - Dell Pro Max with GB10Dell · AI Appliances
Fits at FP16 (~30GB) but limited bandwidth makes token generation slow for a 14B model.
FP16 · ~30GBRuns slowly - AMD Ryzen AI Max Mini PC (Strix Halo class)AMD · Mini PCs
Fits at FP16 (~30GB) but limited bandwidth makes token generation slow for a 14B model.
FP16 · ~30GBRuns slowly - Apple Mac mini (M4 Pro)Apple · Apple Silicon
Fits at FP16 (~30GB) but limited bandwidth makes token generation slow for a 14B model.
FP16 · ~30GBRuns slowly - Small Business Mini PC (reference profile)Reference · Mini PCs
Fits at Q8_0 (~16GB) but limited bandwidth makes token generation slow for a 14B model.
Q8_0 · ~16GBRuns slowly - Apple Mac mini (M4)Apple · Apple Silicon
Fits at Q8_0 (~16GB) but limited bandwidth makes token generation slow for a 14B model.
Q8_0 · ~16GBRuns slowly - Dual RTX 3060 Local Server (reference profile)Reference · AI Servers
Fits at Q8_0 (~16GB) but limited bandwidth makes token generation slow for a 14B model.
Q8_0 · ~16GBRuns slowly - Intel Arc B580 12GBIntel · Consumer GPUs
Fits only at Q4_K_M with little headroom (~0.6GB) — usable but tight; consider more memory.
Q4_K_M · ~10GBRuns slowly - NVIDIA GeForce RTX 3060 12GBNVIDIA · Consumer GPUs
Fits at Q4_K_M (~10GB) but limited bandwidth makes token generation slow for a 14B model.
Q4_K_M · ~10GBRuns slowly
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