Most buyers arrive at our dedicated GPU hosting already anchored on one card from a tutorial or forum post. That anchoring is rarely right for their workload. This ladder lays out every GPU we host in order, what you can do on it, and what the next step up actually buys you.
The Tiers
Entry Tier
RTX 3050 (6 GB) – our cheapest option. Phi-3-mini INT4, Whisper small, tiny embedding servers. Hobby scale.
RTX 4060 (8 GB) – our step-up entry. Mistral 7B INT4, Llama 3 8B INT4 with short context, SDXL with aggressive optimisation.
RTX 5060 Blackwell (8 GB) – same capacity, much faster. GDDR7 bandwidth plus FP8 tensor cores. For small-model decode speed.
Mid Tier
RTX 4060 Ti 16GB – the entry into serious AI. Llama 3 8B at INT8 with headroom, Mistral 7B FP16, SDXL production. Below this tier you are quantising constantly.
AMD RX 9070 XT (16 GB) – AMD gaming-class card with strong compute. Same VRAM class as 4060 Ti on ROCm.
RTX 5080 (16 GB) – Blackwell flagship below 32 GB. Same capacity but nearly double the bandwidth of the 4060 Ti.
Large Tier
RTX 3090 (24 GB) – the value pick for memory-hungry workloads. High bandwidth, mature CUDA. Still the best cost-per-GB in most cases.
Intel Arc Pro B70 (32 GB) – 32 GB without Nvidia pricing. IPEX-LLM / OpenVINO stack.
RTX 5090 (32 GB) – Blackwell with real capacity. Fastest consumer-class card for AI. 70B INT4 fits.
AMD Radeon AI Pro R9700 (32 GB) – workstation AMD with 32 GB. ROCm stack, good SDXL performance.
Ryzen AI Max+ 395 (96 GB unified) – APU with huge shared memory. Bandwidth-limited but fits models nothing else can on a single box.
Flagship Tier
RTX 6000 Pro (96 GB) – the top of the stack. 70B at INT8, Mixtral 8x22B INT4, batched high-concurrency serving. If a workload does not fit here, you need multiple cards.
Climb Only When the Workload Demands It
Our team sizes servers to your actual model and concurrency – no upsell on capacity you will not use.
Browse GPU ServersClimbing Rules
Two rules save money. First: do not step up until the current tier is VRAM-limited or latency-limited in a way users can measure. Buying a 5090 for a workload that fits on a 4060 Ti is waste. Second: step up by the capacity line that matters. The jump from 16 GB to 24 GB unlocks different models than the jump from 24 GB to 32 GB, which is smaller than it looks. See VRAM per pound for the economic view.
For specific head-to-head matchups on each rung see 4060 Ti vs 5060, 3090 vs 4060 Ti, and 5080 vs 5090.
